Saturday, 26 June 2010
Friday, 25 June 2010
Monday, 21 June 2010
My father passed away in 1998. I got a call from my mother. I asked what I could do. "Just come," she said.
I took the bus from Brandon through northern Ontario, overnight in the dead of winter, to arrive in a couple days and in time to be there.
I'm glad I was able to achieve some kind of reconciliation before he died. We were ICQ buddies, and would chat about all manner of things. I finally got to meet my father the tinkerer and inventor, rather than my father the father.
I had long known that this day would come, as he had been on transplants for something like 20 years. I guess we all know, in our different ways, that it would come.
After the funeral I gathered by brothers and cousins and we went to a bar in South Gloucester. And as I had long planned, I stood up, called for everyone's attention, and told them to drink a beer to my father, because he was a good man, and should be remembered.
My father's birthday was in June, matched up with Father's Day. My mother's is in May, matched up with Mother's Day. It's hard not to think as a child that this was all pre-arranged, when it works out this way.
But I don't think of my father on father's day. I think of him every four years, during the Winter Olympics, to commemorate that long dark journey through the cold and the snow. Knowing that there were many possible worlds in which I would not have made that journey, would not have met my father, would not have ever reached a reconciliation.
People who say the internet doesn't touch people personally know nothing.
(Posted as a reaction to Cogdogblog's post).
The only photo I have of our entire family, taken at my grandaprent's 50th wedding anniversary (thta's their white farmhouse in Clarenceville in the background). From left to right: Gordon, Allan, my mother (Beverly), my father (Bernard), Bill, John and myself (Stephen).
I took the bus from Brandon through northern Ontario, overnight in the dead of winter, to arrive in a couple days and in time to be there.
I'm glad I was able to achieve some kind of reconciliation before he died. We were ICQ buddies, and would chat about all manner of things. I finally got to meet my father the tinkerer and inventor, rather than my father the father.
I had long known that this day would come, as he had been on transplants for something like 20 years. I guess we all know, in our different ways, that it would come.
After the funeral I gathered by brothers and cousins and we went to a bar in South Gloucester. And as I had long planned, I stood up, called for everyone's attention, and told them to drink a beer to my father, because he was a good man, and should be remembered.
My father's birthday was in June, matched up with Father's Day. My mother's is in May, matched up with Mother's Day. It's hard not to think as a child that this was all pre-arranged, when it works out this way.
But I don't think of my father on father's day. I think of him every four years, during the Winter Olympics, to commemorate that long dark journey through the cold and the snow. Knowing that there were many possible worlds in which I would not have made that journey, would not have met my father, would not have ever reached a reconciliation.
People who say the internet doesn't touch people personally know nothing.
(Posted as a reaction to Cogdogblog's post).
The only photo I have of our entire family, taken at my grandaprent's 50th wedding anniversary (thta's their white farmhouse in Clarenceville in the background). From left to right: Gordon, Allan, my mother (Beverly), my father (Bernard), Bill, John and myself (Stephen).
Friday, 18 June 2010
The second installment in my contribution to the iDC discussion.
My call to arms of the previous week didn't really attract the attention of this list. Whether that be because it was either trivial or implausible I cannot judge. But it seems to me that "a society built, not on the basis of a propagation of ideas, but rather, on the basis of a gathering of them" captures something important in the changes that are happing in our culture.
The concept of the course is one point where this can be seen. What has happened to the course over the years has also happened to other parts of our culture, and the current concept of the course has become so entrenched that we cannot conceive of it being something else, but rather, only more of what it has currently become.
Let me explain. The 'course' was originally a series of lectures given by a professor at a university, sometimes at the invitation of a student or academic society, and sometimes on his own initiative. The actual academic work being undertaken by a student, understood as a person who was "reading in such-and-such", typically under the direction of one of these professors, was completely separate. Courses were resources, rather like books, that could be used to extend their knowledge and suggest new ways of thinking, not a body of content intended to be learned and remembered.
Even at the lower grades, the idea of the course had little meaning. Read texts such as the autobiography of John Stuart Mill and we see that while there was a certain body of material - classical languages, rhetoric and logic, history, geography, science and mathematics - that was expected to be learned, an education was a continuous and fluid process of teaching and learning, not an assemblage of 'courses', much less 'credits' (or that atrocity, the 'credit-hour'). These are inventions that came into being only with the industrialization of education, with the division of the labour of teaching, the devolution from an individual tutor who specialized in the student, to a series of tutors who specialized in the subject.
But as the use of the course expanded, the infrastructure and way of talking about an education gradually grew to be centered on the course itself. With individual courses came individual textbooks designed for specific courses, and with distance education came complete course packages with textbooks and designed learning packages describing sequences of activities and interactions. The practice of the lecture, once an almost spontaneous act of creativity, became one of delivering a standard set of learning materials, conformant with a course outline, and congruent with learning outcomes that would be measured in a summative student evaluation at regular intervals.
Thus, when we think of the future of the course, it is tempting to think of an acceleration of this model, where the 'deliver' becomes more and more efficient, where 'textbooks' and 'course packages' are combined into easily packaged multimedia entities, and where the concept of 'talking a course', far from being an interesting and engaging set of genuinely academic work, has become nothing more than the demonstration of mastery of a set of competences known, defined, and well-described far in advance of any actual learning experience.
And so we get exactly this prediction of what the concept a course will become: "“Do you really think in 20 years somebody’s going to put on their backpack drive a half hour to the University of Minnesota from the suburbs, hault their keester across campus and listen to some boring person drone on about Spanish 101 or Econ 101? . . . Is there another way to deliver the service other than a one size fits all monopoly provided that says show up at nine o’clock on Wednesday morning for Econ 101, can’t I just pull that down on my iPhone or iPad whenever the heck I feel like it from wherever I feel like, and instead of paying thousands of dollars can I pay 199 for iCollege instead of 99 cents for iTunes, you know?" As posted by Trebor Scholz
And a lot of stuff in our world has become like that. Books, once originally hand-written (and not so long ago either) are now dictated off the cuff to some secretary, or are assembled using some link-catching software (cf Steven Johnson ) or some other industrial-age process that involves only a small amount of actual authorship and a great deal of assembling, packaging and marketing (I think also of Jaron Lanier observing that creativity today is being replaced by assembly of many small bits of not-so-creative content ). Music is based on synthed voices, drum machines, and packaging and distribution contracts.
It is not enough to say these things are hard. It is not enough to say "Quality online courses are in fact neither cheap nor easy to teach." Because this just reifies the original idea, that what we are producing is some sort of packaged and marketed version of something that was once earlier a much more continuous and much more human process. Saying that "music is hard to create" is neither true nor useful. The same criticism applies to courses. It's not true because, with good technology, things that were really hard are now very accessible to people. I can, in a matter of seconds, lay down a really good and creative backing beat with Roc. http://aviary.com/tools/music-creator Putting together a 'course', for anyone with some degree of subject matter expertise, is no more difficult. There's nothing wrong with Hubert Dreyfus's lectures in iTunes University. They are perfectly good 'courses' and a great many people have already learned a great deal from them.
What is wrong with the idea of "instead of paying thousands of dollars can I pay 199 for iCollege" is not that you can't get a course for that kind of money - you can - but rather the concurrent acceptance of a model that has been developing for decades to the effect that one's education, one's self, is something that is consumed, passively, rather than created actively. And even that's not quite it, because people who are listening to Dreyfus every morning on their iPod are actually actively engaged in supporting their own learning.
What is missing here is the answer to the question, "Is this all there is?" Is 'getting existentialism' now equivalent to listening to Dreyfus on tape? Well, no - but that's not because creating a course is hard. Rather, it has everything to do with the learner's investment and contribution to the act of learning. Sitting in the lecture hall, listening to one of the greats hold forth on a series of questions that you helped articulate and pose, engaged in a series of lectures that you helped organize, because they fed into a research programme that you created and implemented, is very different than listening to Hubert Dreyfus on tape, not because it's hard for Hubert Dreyfus to do his part, but because it's hard for you to do your part. We don't (as we all know, right?) consume an education, but our education system has become based on the model of consumption, so much so that even the critics of it can articulate only about how hard it is to create the consumable.
This is why we - George and I and David and Alec and Dave and others - are working on opening up education. Not because we think it will reduce the cost of the consumable to zero, not because we think we can package and deliver an education more cheaply and more efficiently, but because we understand that, unless an education is open, unless it's precisely *not* a consumable, it's not an education at all. And while *this* observation, that education is not a consumable, is hardly new or unique, our approach to it appears to have been (though you know if you go back into the history of education you can find a great deal about self-organizing learning communities and the pedagogies based on such models).
We have structured our approach to openness in learning in three stages:
1. Open Content - here we refer to any material that may be of use in the purpose of education, not merely the professional materials that might be produced by educators and publishers, such as looks, learning packages, learning content, learning objects, but also the artifacts created by people generally as evidence of their own learning, blog posts, videos, music, animations, software and the like; and distributed, not in the sense that they are collected and packaged and flaked and formed and sold or distributed through advertiser-based media, but rather, exchanged peer to peer, through a network of connections, as a conversation rather than a commodity. We have all of us offered reams of learning materials online, freely available to all who wish to read them, watch them, listen to them, or to use the to create and share and create anew.
2. Open Instruction - here we refer to the 'lecture' portion of open learning, or rather, the internet analogue of the original lecture described at the top of this post, a series or sequence of activities undertaken by experts (or possibly putative experts) in a field, but conducted not merely so fully-subscribed students at Cambridge or Oxford can attend, but rather, set out into the open, taking advantage of modern streaming and conferencing technology, so that an entire community can attend, the conduct, then, of learning activities and dialogue and reflection in an open forum, engaging learners, and modeling the practice of the discipline or domain. Thus the Connectivism and Connective Knowledge course conducted all its activities, including synchronous class sessions, in a free and open environment, and at its peak was attended by 2200 students, each engaged in a more or less self-determined set of individual activities.
3. Open Assessment - there we refer to the practice of obtaining and displaying credentials demonstrating what one has learned, and therefore of the process and procedures leading to the assessment of such credentials, and instead of maintaining and enforcing a monopoly on the recognition of learning. In Connectivism and Connective Knowledge, for example, we published assignment directions and questions, as well as rubrics for the assessment of these assignments, and stated that any external agency that wished to assess students (who in turn wished to be assessed) attending our course could do so. This, in a given 'course' there is not a single mode of assessment, but can be as many as there are students, and the assessment of individual accomplishment is not only separated from the presentation of course content or the conduct of course instruction, it is independent of it.
This three-fold opening of learning allows anyone with the interest and inclination (and computer connection and time - two factors that cannot be overlooked when considering the widespread applicability of this model) to benefit from the learning we offer, but not to benefit simply as a passive consumer of the learning (such would in one of our connectivist courses be a very poor learning experience indeed, as we have all been told by disgruntled (and putative) 'students'), but as an active participant in the creation of their own learning. It restores the learner's investment and contribution to the act of learning, and does so in the only way that would possibly work, by the elimination of corporate or institutional proprietorship over the instruments of learning. To the extent that learning is produced and owned and sold to the student by a provider, is the extent to which the student fails to realize the benefit of that learning, and must substitute some alternative mechanism of their own.
This is what you see in actual universities and is what is exactly not produced by prepackaged and syndicated lectures. You don't see the learning the students create for themselves, by arguing until the wee hours in pubs, by forming and reforming into clubs and associations and societies, by undertaking projects profound to mundane, from the student newspaper to student government to charitable works to engineering pranks, by forming study circles and reading circles and discussion groups and debating events and even sports and recreation and music and theatre. All these are the education proper that happens in a university system, and what are abstracted out of course packages, and none of these are 'easy' or 'hard' to deliver at greater or lesser quality because these are not delivered at all, but rather are created by the students themselves.
These, indeed, are the things we look for as products of the three degrees of open education - not a demonstration of some learned body of knowledge, not mastery of a true-false test or even the wiring of a definitive essay or passing of an oral exam, but rather, evidence that the facilitation provided - open content, instruction and assessment - have led to the development of these learning activities, in whatever shape or form, by the learners themselves, evidence that they have begun to find and form and work with their own understanding, to create their own infrastructure, to prepare themselves to become practitioners and therefore teachers in their own right. We judge the success of a course not by the grades but by the proliferation of learning activity in its wake, and by that measure, the Connectivism course was significantly successful, having spawned activities and communities that thrive two years later.
None of this, however, is relevant to a community that still sees academic and learning as having to do with the propagation of ideas, and can only view creative acts from the perspective of a publisher or aggregator. A society based on the aggregation of ideas is not one based on the idea of free labour, because the concept of labour applies only is what is produced, as though in a factory, is commoditized and sold, as though a good or a package.
And though this may be hard for anyone involved in the 'production' of knowledge or information or content or learning to understand, it doesn't matter whether the call to arms received any reaction from this list or any other list, because what was important in the call to arms wasn't the propagation of the ideas inside it, Wasn't the marketing and distribution and popularization of those ideas, but the very act of creating those ideas in the first place, a space where designations of 'trivial' or 'implausible' don't even have any meaning, much less relevance. In writing this, I create my own learning, and its meaning is determined, not by the effect it has on you, but by the impact it had on me through the act of its creation. What matters, of the work that I do, is that it help provide, and not hinder, an open space for content, instruction, and assessment.
My call to arms of the previous week didn't really attract the attention of this list. Whether that be because it was either trivial or implausible I cannot judge. But it seems to me that "a society built, not on the basis of a propagation of ideas, but rather, on the basis of a gathering of them" captures something important in the changes that are happing in our culture.
The concept of the course is one point where this can be seen. What has happened to the course over the years has also happened to other parts of our culture, and the current concept of the course has become so entrenched that we cannot conceive of it being something else, but rather, only more of what it has currently become.
Let me explain. The 'course' was originally a series of lectures given by a professor at a university, sometimes at the invitation of a student or academic society, and sometimes on his own initiative. The actual academic work being undertaken by a student, understood as a person who was "reading in such-and-such", typically under the direction of one of these professors, was completely separate. Courses were resources, rather like books, that could be used to extend their knowledge and suggest new ways of thinking, not a body of content intended to be learned and remembered.
Even at the lower grades, the idea of the course had little meaning. Read texts such as the autobiography of John Stuart Mill and we see that while there was a certain body of material - classical languages, rhetoric and logic, history, geography, science and mathematics - that was expected to be learned, an education was a continuous and fluid process of teaching and learning, not an assemblage of 'courses', much less 'credits' (or that atrocity, the 'credit-hour'). These are inventions that came into being only with the industrialization of education, with the division of the labour of teaching, the devolution from an individual tutor who specialized in the student, to a series of tutors who specialized in the subject.
But as the use of the course expanded, the infrastructure and way of talking about an education gradually grew to be centered on the course itself. With individual courses came individual textbooks designed for specific courses, and with distance education came complete course packages with textbooks and designed learning packages describing sequences of activities and interactions. The practice of the lecture, once an almost spontaneous act of creativity, became one of delivering a standard set of learning materials, conformant with a course outline, and congruent with learning outcomes that would be measured in a summative student evaluation at regular intervals.
Thus, when we think of the future of the course, it is tempting to think of an acceleration of this model, where the 'deliver' becomes more and more efficient, where 'textbooks' and 'course packages' are combined into easily packaged multimedia entities, and where the concept of 'talking a course', far from being an interesting and engaging set of genuinely academic work, has become nothing more than the demonstration of mastery of a set of competences known, defined, and well-described far in advance of any actual learning experience.
And so we get exactly this prediction of what the concept a course will become: "“Do you really think in 20 years somebody’s going to put on their backpack drive a half hour to the University of Minnesota from the suburbs, hault their keester across campus and listen to some boring person drone on about Spanish 101 or Econ 101? . . . Is there another way to deliver the service other than a one size fits all monopoly provided that says show up at nine o’clock on Wednesday morning for Econ 101, can’t I just pull that down on my iPhone or iPad whenever the heck I feel like it from wherever I feel like, and instead of paying thousands of dollars can I pay 199 for iCollege instead of 99 cents for iTunes, you know?" As posted by Trebor Scholz
And a lot of stuff in our world has become like that. Books, once originally hand-written (and not so long ago either) are now dictated off the cuff to some secretary, or are assembled using some link-catching software (cf Steven Johnson ) or some other industrial-age process that involves only a small amount of actual authorship and a great deal of assembling, packaging and marketing (I think also of Jaron Lanier observing that creativity today is being replaced by assembly of many small bits of not-so-creative content ). Music is based on synthed voices, drum machines, and packaging and distribution contracts.
It is not enough to say these things are hard. It is not enough to say "Quality online courses are in fact neither cheap nor easy to teach." Because this just reifies the original idea, that what we are producing is some sort of packaged and marketed version of something that was once earlier a much more continuous and much more human process. Saying that "music is hard to create" is neither true nor useful. The same criticism applies to courses. It's not true because, with good technology, things that were really hard are now very accessible to people. I can, in a matter of seconds, lay down a really good and creative backing beat with Roc. http://aviary.com/tools/music-creator Putting together a 'course', for anyone with some degree of subject matter expertise, is no more difficult. There's nothing wrong with Hubert Dreyfus's lectures in iTunes University. They are perfectly good 'courses' and a great many people have already learned a great deal from them.
What is wrong with the idea of "instead of paying thousands of dollars can I pay 199 for iCollege" is not that you can't get a course for that kind of money - you can - but rather the concurrent acceptance of a model that has been developing for decades to the effect that one's education, one's self, is something that is consumed, passively, rather than created actively. And even that's not quite it, because people who are listening to Dreyfus every morning on their iPod are actually actively engaged in supporting their own learning.
What is missing here is the answer to the question, "Is this all there is?" Is 'getting existentialism' now equivalent to listening to Dreyfus on tape? Well, no - but that's not because creating a course is hard. Rather, it has everything to do with the learner's investment and contribution to the act of learning. Sitting in the lecture hall, listening to one of the greats hold forth on a series of questions that you helped articulate and pose, engaged in a series of lectures that you helped organize, because they fed into a research programme that you created and implemented, is very different than listening to Hubert Dreyfus on tape, not because it's hard for Hubert Dreyfus to do his part, but because it's hard for you to do your part. We don't (as we all know, right?) consume an education, but our education system has become based on the model of consumption, so much so that even the critics of it can articulate only about how hard it is to create the consumable.
This is why we - George and I and David and Alec and Dave and others - are working on opening up education. Not because we think it will reduce the cost of the consumable to zero, not because we think we can package and deliver an education more cheaply and more efficiently, but because we understand that, unless an education is open, unless it's precisely *not* a consumable, it's not an education at all. And while *this* observation, that education is not a consumable, is hardly new or unique, our approach to it appears to have been (though you know if you go back into the history of education you can find a great deal about self-organizing learning communities and the pedagogies based on such models).
We have structured our approach to openness in learning in three stages:
1. Open Content - here we refer to any material that may be of use in the purpose of education, not merely the professional materials that might be produced by educators and publishers, such as looks, learning packages, learning content, learning objects, but also the artifacts created by people generally as evidence of their own learning, blog posts, videos, music, animations, software and the like; and distributed, not in the sense that they are collected and packaged and flaked and formed and sold or distributed through advertiser-based media, but rather, exchanged peer to peer, through a network of connections, as a conversation rather than a commodity. We have all of us offered reams of learning materials online, freely available to all who wish to read them, watch them, listen to them, or to use the to create and share and create anew.
2. Open Instruction - here we refer to the 'lecture' portion of open learning, or rather, the internet analogue of the original lecture described at the top of this post, a series or sequence of activities undertaken by experts (or possibly putative experts) in a field, but conducted not merely so fully-subscribed students at Cambridge or Oxford can attend, but rather, set out into the open, taking advantage of modern streaming and conferencing technology, so that an entire community can attend, the conduct, then, of learning activities and dialogue and reflection in an open forum, engaging learners, and modeling the practice of the discipline or domain. Thus the Connectivism and Connective Knowledge course conducted all its activities, including synchronous class sessions, in a free and open environment, and at its peak was attended by 2200 students, each engaged in a more or less self-determined set of individual activities.
3. Open Assessment - there we refer to the practice of obtaining and displaying credentials demonstrating what one has learned, and therefore of the process and procedures leading to the assessment of such credentials, and instead of maintaining and enforcing a monopoly on the recognition of learning. In Connectivism and Connective Knowledge, for example, we published assignment directions and questions, as well as rubrics for the assessment of these assignments, and stated that any external agency that wished to assess students (who in turn wished to be assessed) attending our course could do so. This, in a given 'course' there is not a single mode of assessment, but can be as many as there are students, and the assessment of individual accomplishment is not only separated from the presentation of course content or the conduct of course instruction, it is independent of it.
This three-fold opening of learning allows anyone with the interest and inclination (and computer connection and time - two factors that cannot be overlooked when considering the widespread applicability of this model) to benefit from the learning we offer, but not to benefit simply as a passive consumer of the learning (such would in one of our connectivist courses be a very poor learning experience indeed, as we have all been told by disgruntled (and putative) 'students'), but as an active participant in the creation of their own learning. It restores the learner's investment and contribution to the act of learning, and does so in the only way that would possibly work, by the elimination of corporate or institutional proprietorship over the instruments of learning. To the extent that learning is produced and owned and sold to the student by a provider, is the extent to which the student fails to realize the benefit of that learning, and must substitute some alternative mechanism of their own.
This is what you see in actual universities and is what is exactly not produced by prepackaged and syndicated lectures. You don't see the learning the students create for themselves, by arguing until the wee hours in pubs, by forming and reforming into clubs and associations and societies, by undertaking projects profound to mundane, from the student newspaper to student government to charitable works to engineering pranks, by forming study circles and reading circles and discussion groups and debating events and even sports and recreation and music and theatre. All these are the education proper that happens in a university system, and what are abstracted out of course packages, and none of these are 'easy' or 'hard' to deliver at greater or lesser quality because these are not delivered at all, but rather are created by the students themselves.
These, indeed, are the things we look for as products of the three degrees of open education - not a demonstration of some learned body of knowledge, not mastery of a true-false test or even the wiring of a definitive essay or passing of an oral exam, but rather, evidence that the facilitation provided - open content, instruction and assessment - have led to the development of these learning activities, in whatever shape or form, by the learners themselves, evidence that they have begun to find and form and work with their own understanding, to create their own infrastructure, to prepare themselves to become practitioners and therefore teachers in their own right. We judge the success of a course not by the grades but by the proliferation of learning activity in its wake, and by that measure, the Connectivism course was significantly successful, having spawned activities and communities that thrive two years later.
None of this, however, is relevant to a community that still sees academic and learning as having to do with the propagation of ideas, and can only view creative acts from the perspective of a publisher or aggregator. A society based on the aggregation of ideas is not one based on the idea of free labour, because the concept of labour applies only is what is produced, as though in a factory, is commoditized and sold, as though a good or a package.
And though this may be hard for anyone involved in the 'production' of knowledge or information or content or learning to understand, it doesn't matter whether the call to arms received any reaction from this list or any other list, because what was important in the call to arms wasn't the propagation of the ideas inside it, Wasn't the marketing and distribution and popularization of those ideas, but the very act of creating those ideas in the first place, a space where designations of 'trivial' or 'implausible' don't even have any meaning, much less relevance. In writing this, I create my own learning, and its meaning is determined, not by the effect it has on you, but by the impact it had on me through the act of its creation. What matters, of the work that I do, is that it help provide, and not hinder, an open space for content, instruction, and assessment.
Tuesday, 15 June 2010
Ontology-centric Knowledge Discovery in a Contact Centre for Technica Product Support
Brad Shoebottom, Innovatia
This paper outlines a technical support product that helps people find answers to questions for customers. It uses an ontology where named elements in tech nical documents are annotated, where this is derived from an ontology via a web services framework.
Description of a pilot study, of searchers with up to four terms. Phase 1 - ontology and usability test. This has been completed, and asked whether users can find answers to some common queries. Phase 2, scenario testing, begins next month.
The pilot survey results showed that people were finding results more quickly, including on the visual query. There were testing challenges, especially in finding enough people. Extra time was needed to gather baseline results with the old toolset, which did not exist.
Performance metrics: productive vs non-productive time, first call resolution, case closed timeframe, filtration rate, and revenue model.
The benefit for New Brunswick is - if we can save, on the technical support side, we can save a lot of money. NB employs 18,000 people in contact centres. The projected savings is 26 percent. It's a reusable methodology applicable across multiple platforms.
Algorithm to populate Telecom domain OWL-DL ontology with A-box object properties....
A Kouznetsov, UNB-SJ
Why text mining? A significant amount of time is spent populating ontologies. We want to reduce the workload. Cjhallenges - complications - multiple properties between keywords. Eg. you have two key words, Sam and Mary - you want to knwo what the relations are between them, but there may be more than one.
Methodology - ontology-based information retrieval applies natural language processing (NLP) to link text segments and named entities. Each candidate linkage is given a (weighted) score. Properties are scored by using the distance between co-occurring terms.
Man tools used were PATE/JAPE for text mining and OWLAPI for the ontology. http://en.wikipedia.org/wiki/Jape_%28software%29
(Boxes and levels diagram - pre-processing, text segments processing, ontology populatoon).
(Flow chart diagram with the same information)
Basically, the ontology seeds the search for candidates, which after scoring may result in new candidates for the ontology, as a result of co-occurrence.
(Flow chart of the co-occurrence based scores generator) uses a synonyms list.
Diagram of extensible data model for text and a more complicate dversion for tables.
(This talk is being given in seni-literate sentence fragments - the speaker looks at the slide, rads part of it, then adds a half-sentence expanding on it- SD)
Sample sentence: "We always base our decision of this part, but partially use this."
List of primary scoring methods for table segments. (This doesn't actually tell us how the scoring happens, consisting of overlapping circles representing the different terms).
Sentence scoring:
sentencescopre = 1/(distance+1)+bonus
(for example)
The mechanism is based on domain and range (domain relates to subject, range to object).
Quote from slide:
Normalization - Log(1.0+(NSD+1.0/Cd)*(NSR+1.0/Cr))
NSD- number of sentencs domain occurred
Cd Domain synonyms list cardinality
NSR - number of sentences range ocurred
Cr - range synomyms list cardinality
How can we evaluate our prediction? (or what? I don't know - this is the first mention of a prediction in the talk - SD) "Focus on positive class recall and positive class precision." Best results when both sentences and tables joined.
(I give up... this is just an awful awful presentation)
Leverage of OWL-DL axioms in a Contact centre for Technical {Product Support
A Kouznetsov, UNB-SJ
(ack, no, it's the same speaker!)
The 'semantic assistant' frameowkr was developed at Concordia (a remarkably clear slide, must be borrowed). It's where semantic analysis services relevant to the user's current tasks are offered directly within the desktop applictaion. It relies on an OWL ontology model.
(layers and boxes diagram, sideways)
We extended the system with an ontology and axiom extractor.
(New speaker - Chris Baker)
The point is that we are able to leverage mappings between synonyms and the name density in the client interface. It leverages any own ontology, Now we're looking at different plugins, so we don't have to rely on OpenOffice as a client. The idea is to help people in the middle of a workflow to tell them that somebody has already written a document about this.
The Semantic Assistant framework can be downloaded from Concordia - it's open source. http://www.semanticsoftware.info/
Basically we picked up a technology and played around with it.
NLP pipeline for protein mutation knowledgebase construction
Jonas B. Laurila, UNB-SJ
Knowledge about mutations is critical for many applications, eg. biomedicine. Protein mutations are described in scientific literayure. Today, databases are curated manually, but the amount of literature is growing faster than humans can populate databases.
There is a typical description of mutations,. Terms include the mutation, directionality of impact (increased, reduced), and the property. Also you need information about protein name, gene name, and organism name, which is usually in the title of the paper.
We created an upper-level ontology with proteins and different kinds of mutations.
(Diagram of the framework, consisting of a series of gates)
For example, named-entity recognition. We use a gazateer list based on SwissProt, and store mappings in a database. To find mutation mentions, we use rules rather than a gazateer, normalizing into a wNm-format. We identify protein functions from noun phrases extracted with MuNPEx (a NP extractor). We use Jape rules to extract rates and quantities.
(This presenter is a low-talker and mumbler and sometimes gives up on sentences prtway through with a 'whatever')
They also need to correctly position the mutation in the gene sequence - tey use regular expressions to identify it (because the writer sometimes clips part of the sequence or changes the numbeing scheme). mSTRAPviz is used to provide a visualization.
Once that is done, you can do queries. Eg. find all mutations on so-and-so that do not have an impact on the such-and-such activity. You can also do mutation grounding performance (ie., the num,ber of correctly grounded mutations overall).
What's next is to modularize the work into web services, database recreation, and to reuse the work in phenotype prediction algorithms.
C-BRASS - Canadian Bioinformatics Resources ans Semantic Services
Chris Baker, UNB-SJ
First widespread deployment of a grid framework where the messages are meaningful to machine interpreters. The idea is to create toolkits to 'lift' legacy resources into a semantic web framework.
Currently, there is a low update of semantic web integration in the bioinformatics community. This is because of challenges in implementing solutions, and a gap between what the services offer and what they need. The lack of sementics in service discovery makes them hard to discover and use. Semantic web services are designed to list services based on the meanings of the inputs and outputs.
SWS frameworks describe input & output data structures, operations of the web service. Eg. BioMoby is a service type ontology. Single term semantics are too simplistic, process descriptions are too complex. So we want to model the inputs and outputs in ontological models.
An end-user community doesn't have a process-model or business-model in mind when they're searching. They execute a BLAST alignment not because they want to run a sequence similarity matrix, but because they are looking for a certain sort of output. So ontologies of inputs and outputs are generated by the service.
SADI - Semantic Automated Discover and Integration - a set of best practices for data representation. Eg: my service consumes OWL individals of class #1 and returns OWL individuals of class #2.
(chart of some of the SADI recommendations)
How this works - a query-specific database is being generated dynamically as the query is being processed. Eg. 'find gene ontologies related to Parkinsons disease'. It does to the registry and looks for specific predicates, finds the associates services, and pulls back the information for the database that will address the query.
(Better presentation, but still a lot of slide reading)
Deliverable: SADI semantic web service framework as a candidate recommendation, a set of core ontologies in biomedical domain, and a costing model for future semantic web service providers, defining establishment and maintence costs for migrating non-semantic data.
Semantic Spaces for Communication and Coordination
Omair Shafig, Universiy of Calgary
Semantic spaces - the idea comes from the factthat today's web services are not following the principles of web communication. We services communicate in a synchronous manner, but the web should be stateless. We want in a similar way to create a communication, coordination and storage infrastructure for services over the web. This would be basically a single, globally shared semantic space.
We would realize this by joining semantics and speace-based technologies (as in 'triple-=space' not outer space). The semantic space is accessed and fed by semantic space kernels. These kernels take into account the coordination of different data stores and coordinate in a peer-to-peer fashion, and present a single unified access to users.
(Chart of existing technologies - existing technologies are limited in semantic support, query processing and knowledge support).
Requirements:
- persistent data storage
- communication - many-to-many machine communication
- information publishing
- globally accessible - accessed anywhere, anytime
- subscription and notification - subscribe to particular data
- information retrieval, ie., search/querying
- decoupling in communication
- semantic annotation of data
- reliability - alternative paths
Semantic Space Kernel - these are the entities that present the single point of access to users. It should provide layers (what else?) for:
- User API / managemetn API
- Publish-API
- kernel-to-kernel communication
- data access
The semantic space API would include operations such as 'publish triples', 'read triples', subscribe and unsubscribe, create, execute and delete transactions, etc.
We want to use the infrastrcuture to provide semantic spaces for storage, eg., semantic descriptions of web services, monitoring data, intermediate event processing information, service compositional data, etc. Semantic based event driven and publish-subscribe mechanisms facilitate communication. Also would provide space-based coordination of a service bus platform.
We applied a proposed solution as a reference implementation of the WSMO conceptual model called Web Service Execution Environment (called WSMX). The semantic space itself is available as a web service. Services accessing the semantic space could also be services, so, for example, you could use someone else's 'discovery' service to access the semantic space.
We could also ground and store WSMO objects (semantic descriptions of web services) in the semantic space.
To bind existing services to semantic space, we recommend three changes to WSDL. First, to change the transport mechanism, then second to encode SOAP messages as semantic space RDF, and third, the address location. http://www.soa4all.eu
Question - what about spammy content? http://seekda.com
(It checks services to see whether they are working or not)
(SD - this isn't a solution at all)
other comment - weight by trust
Stephen - 'the false triple problem'
Collaboration as a Service
Sandy Liu, NRC
This is a case study based on real systems and real platforms.
Collaboration is a coordinated collection of activities performd by collaborators in order to achieve a set of common goals. It is action-oriented, goal-oriented, invoves a team, and is coordinated. Collaborators could be agents, machines, organizations or institutes, or inviduals.
The simplest model is the 3C model (Ellis, 1991). The Cs are cooperation, communication, coordinaton. There are ontologies for each, such as the cooperation ontology (Oliveira, et. al. 2007, 'Towards a Collaboration Ontology').
Key outcomes can be defined in six tuples: coordination, outcome, goals, collaborators, resources and activities, all linked to collaboration.
In collaboration as a Service, the idea is that a request comes in, CaaS coordinates everything, and the outcome is collaboration (this is a very oversimplified model). Subservices would manage coordination, collaborators, etc.
In the virtual organization, it's the flexible, secure, coordinated resource sharing among dynamic collections of individuals. This is based on work in the 90s. Architects, for example, will often have people doing field work, they will have technical people in the office, stone masons elsewhere, etc.
Or a health service training, where two students discuss a case, more to a mannequin to try it out, and then debrief with the class. To support a simple scenario even like this you have to support a large number of connections. So we have built something called SAVOIR in order to support this, to manage users, resources, sessions and workflow.
SAVOIR - Service-oriented Architecture for Virtual Organization Infrastructure and Resources. It provides a single entry point for provisioning services and tools. It's generic to different types of resources.
SAVOIR was based on similar concepts - Software as a Service, Infrastructure as a Service, and then, finally, Collaboration as a Service.
(Diagram of system overview)
The front end of SAVOIR is a web-based front end. Eg. you can create a session based on the tools available.
The messgae bus - hosts the management services. It handles messages coming from many different types of transport protocols - http, tcp, jms, sms. Within SAVOIR we use JMS messages. In order to talk to SAVOIR we defined a set of specifications; there's a bus interface that talks to the device in its native language. Now with the bus interface, you can talk to many different transport protocols.
The SAVOIR messaging specification defines an asynchronous communication. Each message has to be acknowledged back to SAVOIR. There is a session manger that is rules based, using Drools. Sessions have 'authoring time', 'run time'. A session is an instance of a scenario.
(Diagram of scenarios)
(Video demonstration) http://www.hsvo.ca
SAVOIR can send out messages to the devices. The devices have different states - 'inactive', 'authenticated', 'loaded', 'running', 'paused', 'stopped'. When messages for a device are received, it looks at the state of the device, and if the device is not available, the message is cached and waits for the message. Various messages require the device to be in certain states. These are defined by rules.
The session manager acts like a facade, that take sin messages, provides a new session, starts and stops sessions, triggers the start rule, and delegrates responsibilities to other components.
(Flow chart with message flow)
The Intelligent City Project: Transit Info
William McIver, Jr., NRC
I am based in people-centered technologies, so we're not directly related to semantic services. But we have access to them. The system was inspired by a case in Toronto where the driver diod not consistently announce the stops.
The idea is to use some semantic technologies to address this issue. Here in Fredericton we have a unique research environment in which to do this work - free city 802.11 Wireless (Fred-E-Zone), as well as Red Ball internet iBurst IEEE 802.20 (which is what we ended up using for the transit info project). And this fits into a wider transit infosystem project, for example, real-time information provided by audio to bus riders.
(Presenters *must* learn to speak to the audience, rather than speaking to their slides)
An early prototype involved the developmet of a bus locater architecture. It used 802.11 to locate buses (the 802.20 wasn't available yet). The buses carried GPS locators, using a Java VM and published info using JSON. This project outlined issues involved in collecting data, but as expected, communications would often break down.
By showing this work we met up with red ball internet, who had been working on a similar system. We ended up with a system that supports various types of transit system. It's a comprehensive set of services offering support for both riders and administratprs. It's a RESTful implementation based on Ruby on Rails (using Sinatra, a smaller-footprint version of Rails).
(System architecture diagram with tiny tiny unreadable text)
We use the 'service day schema' to collect real-time data from the vehicles. Some reports were coming in every 5 seconds, others every 15 seconds (these can be changed). There is also a storage schema to allow management to manage the buses, see if they're late, maybe to add routes, etc. There are also some remote services - the key opne focuses on SMS as an alternate interface to the web interface. This was what was good about using a RESTful approach, once we had the scheme worked out we wcould repurpose one service to add another.
All the buses in the Moncton system have been outfitted with vehicle peers. Red Ball is commercializing this technology. The fleet server manages the web user interfaces, kiosks, etc. The vehicle peers can respond to requests - they report GPS data, predict arrival times, announce next stops, and display next stops.
(Image of Moncton 'Bus Catcher')
Photos of bus kit, with aliks PC engine (a-licks?) The device also provides the mobile hot spots on the buses.
Lessons learned:
architecture is more extensible by constraining it to a stateless syetem of resources (ie., RESTful). CRUD via HTTP is very intuitive and easily maintained. System integration with secuirity can be accomplished using HTTP methods. Representation of resources was easily manipulated to send HTML, JSON, XML. Custom mime types could be created for specialized data. Higher level features are easily added by composing low-level features.
Brad Shoebottom, Innovatia
This paper outlines a technical support product that helps people find answers to questions for customers. It uses an ontology where named elements in tech nical documents are annotated, where this is derived from an ontology via a web services framework.
Description of a pilot study, of searchers with up to four terms. Phase 1 - ontology and usability test. This has been completed, and asked whether users can find answers to some common queries. Phase 2, scenario testing, begins next month.
The pilot survey results showed that people were finding results more quickly, including on the visual query. There were testing challenges, especially in finding enough people. Extra time was needed to gather baseline results with the old toolset, which did not exist.
Performance metrics: productive vs non-productive time, first call resolution, case closed timeframe, filtration rate, and revenue model.
The benefit for New Brunswick is - if we can save, on the technical support side, we can save a lot of money. NB employs 18,000 people in contact centres. The projected savings is 26 percent. It's a reusable methodology applicable across multiple platforms.
Algorithm to populate Telecom domain OWL-DL ontology with A-box object properties....
A Kouznetsov, UNB-SJ
Why text mining? A significant amount of time is spent populating ontologies. We want to reduce the workload. Cjhallenges - complications - multiple properties between keywords. Eg. you have two key words, Sam and Mary - you want to knwo what the relations are between them, but there may be more than one.
Methodology - ontology-based information retrieval applies natural language processing (NLP) to link text segments and named entities. Each candidate linkage is given a (weighted) score. Properties are scored by using the distance between co-occurring terms.
Man tools used were PATE/JAPE for text mining and OWLAPI for the ontology. http://en.wikipedia.org/wiki/Jape_%28software%29
(Boxes and levels diagram - pre-processing, text segments processing, ontology populatoon).
(Flow chart diagram with the same information)
Basically, the ontology seeds the search for candidates, which after scoring may result in new candidates for the ontology, as a result of co-occurrence.
(Flow chart of the co-occurrence based scores generator) uses a synonyms list.
Diagram of extensible data model for text and a more complicate dversion for tables.
(This talk is being given in seni-literate sentence fragments - the speaker looks at the slide, rads part of it, then adds a half-sentence expanding on it- SD)
Sample sentence: "We always base our decision of this part, but partially use this."
List of primary scoring methods for table segments. (This doesn't actually tell us how the scoring happens, consisting of overlapping circles representing the different terms).
Sentence scoring:
sentencescopre = 1/(distance+1)+bonus
(for example)
The mechanism is based on domain and range (domain relates to subject, range to object).
Quote from slide:
Normalization - Log(1.0+(NSD+1.0/Cd)*(NSR+1.0/Cr))
NSD- number of sentencs domain occurred
Cd Domain synonyms list cardinality
NSR - number of sentences range ocurred
Cr - range synomyms list cardinality
How can we evaluate our prediction? (or what? I don't know - this is the first mention of a prediction in the talk - SD) "Focus on positive class recall and positive class precision." Best results when both sentences and tables joined.
(I give up... this is just an awful awful presentation)
Leverage of OWL-DL axioms in a Contact centre for Technical {Product Support
A Kouznetsov, UNB-SJ
(ack, no, it's the same speaker!)
The 'semantic assistant' frameowkr was developed at Concordia (a remarkably clear slide, must be borrowed). It's where semantic analysis services relevant to the user's current tasks are offered directly within the desktop applictaion. It relies on an OWL ontology model.
(layers and boxes diagram, sideways)
We extended the system with an ontology and axiom extractor.
(New speaker - Chris Baker)
The point is that we are able to leverage mappings between synonyms and the name density in the client interface. It leverages any own ontology, Now we're looking at different plugins, so we don't have to rely on OpenOffice as a client. The idea is to help people in the middle of a workflow to tell them that somebody has already written a document about this.
The Semantic Assistant framework can be downloaded from Concordia - it's open source. http://www.semanticsoftware.info/
Basically we picked up a technology and played around with it.
NLP pipeline for protein mutation knowledgebase construction
Jonas B. Laurila, UNB-SJ
Knowledge about mutations is critical for many applications, eg. biomedicine. Protein mutations are described in scientific literayure. Today, databases are curated manually, but the amount of literature is growing faster than humans can populate databases.
There is a typical description of mutations,. Terms include the mutation, directionality of impact (increased, reduced), and the property. Also you need information about protein name, gene name, and organism name, which is usually in the title of the paper.
We created an upper-level ontology with proteins and different kinds of mutations.
(Diagram of the framework, consisting of a series of gates)
For example, named-entity recognition. We use a gazateer list based on SwissProt, and store mappings in a database. To find mutation mentions, we use rules rather than a gazateer, normalizing into a wNm-format. We identify protein functions from noun phrases extracted with MuNPEx (a NP extractor). We use Jape rules to extract rates and quantities.
(This presenter is a low-talker and mumbler and sometimes gives up on sentences prtway through with a 'whatever')
They also need to correctly position the mutation in the gene sequence - tey use regular expressions to identify it (because the writer sometimes clips part of the sequence or changes the numbeing scheme). mSTRAPviz is used to provide a visualization.
Once that is done, you can do queries. Eg. find all mutations on so-and-so that do not have an impact on the such-and-such activity. You can also do mutation grounding performance (ie., the num,ber of correctly grounded mutations overall).
What's next is to modularize the work into web services, database recreation, and to reuse the work in phenotype prediction algorithms.
C-BRASS - Canadian Bioinformatics Resources ans Semantic Services
Chris Baker, UNB-SJ
First widespread deployment of a grid framework where the messages are meaningful to machine interpreters. The idea is to create toolkits to 'lift' legacy resources into a semantic web framework.
Currently, there is a low update of semantic web integration in the bioinformatics community. This is because of challenges in implementing solutions, and a gap between what the services offer and what they need. The lack of sementics in service discovery makes them hard to discover and use. Semantic web services are designed to list services based on the meanings of the inputs and outputs.
SWS frameworks describe input & output data structures, operations of the web service. Eg. BioMoby is a service type ontology. Single term semantics are too simplistic, process descriptions are too complex. So we want to model the inputs and outputs in ontological models.
An end-user community doesn't have a process-model or business-model in mind when they're searching. They execute a BLAST alignment not because they want to run a sequence similarity matrix, but because they are looking for a certain sort of output. So ontologies of inputs and outputs are generated by the service.
SADI - Semantic Automated Discover and Integration - a set of best practices for data representation. Eg: my service consumes OWL individals of class #1 and returns OWL individuals of class #2.
(chart of some of the SADI recommendations)
How this works - a query-specific database is being generated dynamically as the query is being processed. Eg. 'find gene ontologies related to Parkinsons disease'. It does to the registry and looks for specific predicates, finds the associates services, and pulls back the information for the database that will address the query.
(Better presentation, but still a lot of slide reading)
Deliverable: SADI semantic web service framework as a candidate recommendation, a set of core ontologies in biomedical domain, and a costing model for future semantic web service providers, defining establishment and maintence costs for migrating non-semantic data.
Semantic Spaces for Communication and Coordination
Omair Shafig, Universiy of Calgary
Semantic spaces - the idea comes from the factthat today's web services are not following the principles of web communication. We services communicate in a synchronous manner, but the web should be stateless. We want in a similar way to create a communication, coordination and storage infrastructure for services over the web. This would be basically a single, globally shared semantic space.
We would realize this by joining semantics and speace-based technologies (as in 'triple-=space' not outer space). The semantic space is accessed and fed by semantic space kernels. These kernels take into account the coordination of different data stores and coordinate in a peer-to-peer fashion, and present a single unified access to users.
(Chart of existing technologies - existing technologies are limited in semantic support, query processing and knowledge support).
Requirements:
- persistent data storage
- communication - many-to-many machine communication
- information publishing
- globally accessible - accessed anywhere, anytime
- subscription and notification - subscribe to particular data
- information retrieval, ie., search/querying
- decoupling in communication
- semantic annotation of data
- reliability - alternative paths
Semantic Space Kernel - these are the entities that present the single point of access to users. It should provide layers (what else?) for:
- User API / managemetn API
- Publish-API
- kernel-to-kernel communication
- data access
The semantic space API would include operations such as 'publish triples', 'read triples', subscribe and unsubscribe, create, execute and delete transactions, etc.
We want to use the infrastrcuture to provide semantic spaces for storage, eg., semantic descriptions of web services, monitoring data, intermediate event processing information, service compositional data, etc. Semantic based event driven and publish-subscribe mechanisms facilitate communication. Also would provide space-based coordination of a service bus platform.
We applied a proposed solution as a reference implementation of the WSMO conceptual model called Web Service Execution Environment (called WSMX). The semantic space itself is available as a web service. Services accessing the semantic space could also be services, so, for example, you could use someone else's 'discovery' service to access the semantic space.
We could also ground and store WSMO objects (semantic descriptions of web services) in the semantic space.
To bind existing services to semantic space, we recommend three changes to WSDL. First, to change the transport mechanism, then second to encode SOAP messages as semantic space RDF, and third, the address location. http://www.soa4all.eu
Question - what about spammy content? http://seekda.com
(It checks services to see whether they are working or not)
(SD - this isn't a solution at all)
other comment - weight by trust
Stephen - 'the false triple problem'
Collaboration as a Service
Sandy Liu, NRC
This is a case study based on real systems and real platforms.
Collaboration is a coordinated collection of activities performd by collaborators in order to achieve a set of common goals. It is action-oriented, goal-oriented, invoves a team, and is coordinated. Collaborators could be agents, machines, organizations or institutes, or inviduals.
The simplest model is the 3C model (Ellis, 1991). The Cs are cooperation, communication, coordinaton. There are ontologies for each, such as the cooperation ontology (Oliveira, et. al. 2007, 'Towards a Collaboration Ontology').
Key outcomes can be defined in six tuples: coordination, outcome, goals, collaborators, resources and activities, all linked to collaboration.
In collaboration as a Service, the idea is that a request comes in, CaaS coordinates everything, and the outcome is collaboration (this is a very oversimplified model). Subservices would manage coordination, collaborators, etc.
In the virtual organization, it's the flexible, secure, coordinated resource sharing among dynamic collections of individuals. This is based on work in the 90s. Architects, for example, will often have people doing field work, they will have technical people in the office, stone masons elsewhere, etc.
Or a health service training, where two students discuss a case, more to a mannequin to try it out, and then debrief with the class. To support a simple scenario even like this you have to support a large number of connections. So we have built something called SAVOIR in order to support this, to manage users, resources, sessions and workflow.
SAVOIR - Service-oriented Architecture for Virtual Organization Infrastructure and Resources. It provides a single entry point for provisioning services and tools. It's generic to different types of resources.
SAVOIR was based on similar concepts - Software as a Service, Infrastructure as a Service, and then, finally, Collaboration as a Service.
(Diagram of system overview)
The front end of SAVOIR is a web-based front end. Eg. you can create a session based on the tools available.
The messgae bus - hosts the management services. It handles messages coming from many different types of transport protocols - http, tcp, jms, sms. Within SAVOIR we use JMS messages. In order to talk to SAVOIR we defined a set of specifications; there's a bus interface that talks to the device in its native language. Now with the bus interface, you can talk to many different transport protocols.
The SAVOIR messaging specification defines an asynchronous communication. Each message has to be acknowledged back to SAVOIR. There is a session manger that is rules based, using Drools. Sessions have 'authoring time', 'run time'. A session is an instance of a scenario.
(Diagram of scenarios)
(Video demonstration) http://www.hsvo.ca
SAVOIR can send out messages to the devices. The devices have different states - 'inactive', 'authenticated', 'loaded', 'running', 'paused', 'stopped'. When messages for a device are received, it looks at the state of the device, and if the device is not available, the message is cached and waits for the message. Various messages require the device to be in certain states. These are defined by rules.
The session manager acts like a facade, that take sin messages, provides a new session, starts and stops sessions, triggers the start rule, and delegrates responsibilities to other components.
(Flow chart with message flow)
The Intelligent City Project: Transit Info
William McIver, Jr., NRC
I am based in people-centered technologies, so we're not directly related to semantic services. But we have access to them. The system was inspired by a case in Toronto where the driver diod not consistently announce the stops.
The idea is to use some semantic technologies to address this issue. Here in Fredericton we have a unique research environment in which to do this work - free city 802.11 Wireless (Fred-E-Zone), as well as Red Ball internet iBurst IEEE 802.20 (which is what we ended up using for the transit info project). And this fits into a wider transit infosystem project, for example, real-time information provided by audio to bus riders.
(Presenters *must* learn to speak to the audience, rather than speaking to their slides)
An early prototype involved the developmet of a bus locater architecture. It used 802.11 to locate buses (the 802.20 wasn't available yet). The buses carried GPS locators, using a Java VM and published info using JSON. This project outlined issues involved in collecting data, but as expected, communications would often break down.
By showing this work we met up with red ball internet, who had been working on a similar system. We ended up with a system that supports various types of transit system. It's a comprehensive set of services offering support for both riders and administratprs. It's a RESTful implementation based on Ruby on Rails (using Sinatra, a smaller-footprint version of Rails).
(System architecture diagram with tiny tiny unreadable text)
We use the 'service day schema' to collect real-time data from the vehicles. Some reports were coming in every 5 seconds, others every 15 seconds (these can be changed). There is also a storage schema to allow management to manage the buses, see if they're late, maybe to add routes, etc. There are also some remote services - the key opne focuses on SMS as an alternate interface to the web interface. This was what was good about using a RESTful approach, once we had the scheme worked out we wcould repurpose one service to add another.
All the buses in the Moncton system have been outfitted with vehicle peers. Red Ball is commercializing this technology. The fleet server manages the web user interfaces, kiosks, etc. The vehicle peers can respond to requests - they report GPS data, predict arrival times, announce next stops, and display next stops.
(Image of Moncton 'Bus Catcher')
Photos of bus kit, with aliks PC engine (a-licks?) The device also provides the mobile hot spots on the buses.
Lessons learned:
architecture is more extensible by constraining it to a stateless syetem of resources (ie., RESTful). CRUD via HTTP is very intuitive and easily maintained. System integration with secuirity can be accomplished using HTTP methods. Representation of resources was easily manipulated to send HTML, JSON, XML. Custom mime types could be created for specialized data. Higher level features are easily added by composing low-level features.
Monday, 14 June 2010
Introduction to Semantic Web Technologies
Harold Boley, NRC
Presented sample of RDF being used in BBC music site. Wikipedia is using external, public data sets. In short, it is using the web of data as a CMS. More and more data is on the web, and more and more applications rely on that data.
We ned to break out of database silos. We need the infrastructre for a web of data, so data can be interlinked and integrated on the web, so we can start making queries on the whole, not just specific databases.
Example: book. There may be a dataset with ID, author, tite, ec. and separate records for the author and home page, and publisher and home page.
We could export this data as a set of relations. Some of these resources might not have globally visible URLs, though, like the author and publisher information. Still, the relations form a graph. One can export the data generating relations on the fly, and can export part of the data.
We could take this data and merge it with other data, provided we have the same resource id. (Example of english & french metadata being merged). Eg. Combine the 'person' metadata from the book with other sources, such as Wikipedia data (dbpedia.org).
This can be done automatically. This is where ontologies extra rules, etc., come in. These can be relatively simple and small, or huge, or anything in between. And we can ask even more powerful queries of the web of data.
Semantic Web Services
Omair Sgafiq, University of Calgary
Conceptual Model
Next Generation of the web moves from static to dymamic (WWW to WS) and from syntactic to semantic (WWW to semantic web). The final step is combined: Semantic Web Services
Web services are programs accessible over the web. The service oriented architecture (SOA) uses web services as basic building blocks. The current technology includes WDSL, SOAP, UDDI
The web service usage process involves a discovery process where people find services they need. But consumers can't use the descriptions to find services - the description is syntactic, which means nothibg to the user. Also, there's no semantically marked-up content, no support for the semantic web.
To fix this, people have introduced semantic web services. It uses ontologies as the data model, and invokes web services as an integral part. This gives us 'Semantically Enables Service Oriented Architecture' (SESA). We semantically describe client requests, employ mediation techniques, and semantically enhance the complete SOA lifecycle.
Goal-Driven Web Service Usage
There is currently no layer that describes user objectives formally. This requires that service providers lift up semantic descriptions of their services into the web services discover layer, and the objective, described semntically, can be matched to that.
This leads to a definition of WSMO. It combines:
- Goals: objectives that a client wants to achieve
- Ontologies: formally specified terminology used by all other components
- Web Services - seantic descriptions of capabilities and interface
- Mediators - connectors between components with mediation facilities
Ontologies are the data model used throughout. Ontology elements include concepts, attributes, relations, functions, instances, and axioms. These specify the knowledge domain of the service,
The description of the service includes Dublin Core, versioning, quality of sercvice, and financial information, plus functionality, such as choreography and orchestration. (This is *very* high level and - frankly - looks like vaporware - SD).
The capability specification includes pre-conditions, assumptions, post-conditions and effects. Pre-conditions might require, for example, a departure and destination point. The post-condition might be the issuing of a ticket. Choreography tells the service in what order it should perform steps. Orchestration allows service providers to invoke other service providers.
In the objectives section, the client can formally specify their requests. The prupose is to facilitate problem-oriented WS usage, dynamic WS usage, and to provide a central client element. The goal description has three parts: requested capability, requested choreography, and requested orchestration.
The idea is to accept a goal, and decompose it into services. The client has different goal templates, or can write a complete description. First, selection is carried out, then, in operation, the service is actually invoked.
The mediators ensure heterogenty between the data and the processes. This is needed because, eg., clients will write goals in isolation from sevrice providers, leading to the possibility of mismatch. Mediators use a declarative approach, providing a semantic description of resources. Mediation cannot be fully automated.
(There are different approaches to creating semantically-enabled web services).
Semantic Matching
Virendra C. Bhavsar, UNB
Syntactic matching - is basically exact string matching. The result is 'yes' or 'no' (1 or 0). This also includes matching permutations of strings. For example, you can get a measure of similarity between two strings, by dividing number of words in common, with total number of words in string. But this sort of similarity does not have much meaning. We would match, eg. 'electruc chair' and 'committee chair'.
Semantic matching would be useful in a variety of application, for example, e-business, e-learning, matchmaking portals, web services, etc.
Semantic similarity - is an identity relation, between 0 and 1.
Semantic distance, is a matching relationship, between 1 and infinity. It consists of matching of words, short texts, documents, schemas, taxonomy, etc.
Concept similarity in a taxonomy: we can find the similarity of two nodes in a taxonomy tree by tinking about how 'far' they are, as defined (say) by how many nodes we have to traverse. Or the amount of the path length they have in common. Or you might compare the contents of the nodes. Etc. There are different ways to do this.
The motivation for this is to, eg., match online buyers and sellers in a marketplace. The buyer may define a specific set of requests. You need more than flat representations of resources. You need to be able to identity 'part-of', etc. You also need to be able to match properties of buyers and sellers.
(SD - it seems ridiculous to be talking about the 'similarity' of concepts absent any sort of context - similarity depends entirely on salience - because Bhavsar, by using taxonomy trees to measure similarity, is using a 'distance' measure of similarity he is unable to account for salience - see Amos Tversky, Features of Similarity).
Description of partonomy tree similarity engine (based on eduSource work!). See the teclantic protal http://teclantic,ca which uses this type of similarity matching. (This portal is not active now, however.)
Current work includes weighted semantic tree similarity engines, semantic searching, weighted graph similarity engines, multi-core and cluster implementations, and matchmaking portals.
Resource Description Markup Language
Stephen Downes and Luc Belliveau, NRC
My presentation page is http://www.downes.ca/presentation/256
Using Semantic Web Technologies To Unwind Humanities Citations
Mobertson, MAU.
Discussing how semantic web tecnologies are used to manage citations from texts, eg., Latin texts. How do we cite this work? When you're dealing with Latin literature, there are international standards.
Early citations were non-actionable - they would point to text, but in a different work or volume. Eg. John 3:16 - the advantage, though, is that they will be the same everywhere. John 3:16 will be the same no matter what language (The 'John' but will change, but 3:16 won't.
In the Humanities, we use various abbreviations (eg., 'Ar' = Aristophenes in English, Apristotle in French). These were used just to save print. So we want to expand this, so that it's beautiful and intelligble.
We have, for example, Canonical Text Service URNs. Eg. urb:cts:greekLour:tlg0532:tlg001:1:10
In the browser, we will:
- reject the abbreviation wholly
- exploit dbpedia, etc., to provide internationalized (or partially internationalized) versions of the references
- make references dereferencable - ie., you have the ability to click on the reference and see the text immediately.
To integrate CTS URNs and dbpedia, we are using Swiftowlm. We generate the record. Then we generate HTML 5 data divs to display the information, so we can extract it from the database with SPARQL queries.
It also supports a taxonomy of citation (evidence, refutation, etc). This actually allows the citation inline inside the div.
This will also enable the system to replace old-style scanning systems (JSTOR) with the live cittation system. And we will be able to geo- and temporally reference secondary sources by reference to the primary sources they cite. Etc.
(Described how to extract ontologies from text. turns out the method uses pre-supplied lists of terms).
OWL-based Execution of Clinical Practice Guidelines
Borna Jafarpour, Dalhousie
Clinical practice guidelines (CPGs) have a great deal of information. They support decisions, and place constraints on ordering, among other things. (Sample diagram, with flow of tasks).
The motivation behind OWL-based execution is to provide inference-power of OWL to execute CDGs, to integrate everything together. Thus we can develop a workflow ontology in OWL. We can plug in any reasoner that we want.
Problems in OWL
Handling preconditions: 'any' and 'all' easily handled in OWL. 'any k' preconditions (where k is a number) is harder - you have to use a combination of all and some.
If t2 is a precondition, and in turn has precondition t1, OWL can't handle an uncle problem, This is where the precondition t11 is a subtask of t1, it needs to know t1 satisfies the precondition
Also, another problem is restructions of a subset of data values in a range.
Also, there is the 'open world assumption' - from the absence of a statement, a reasoner should not assume that the statement is false.
Thus there needs to be preprocessing.
(more and more stuff to make it work)
Question: if you have to do all this in OWL, is it worth it?
Platform for Ocean Knowledge Management
Ashraf M. Abusharekh, Dalhousie
We need to mix information from oceanography and marine biology on, say, leatherneck turtles. The leatherneck tuyrtle community of interest has members from both these groups.
We developed an SOA+knowlede management approach that supports multiple knpowledge domains, and multiple disparate and distributed users. This is a framework for publishing new scientific capabilities.
(Yes, the presentations really are this vague)
(Big picture with boxes and layers)
POKM relies mostly on the ocean tracking network (OTN). The POKM ontology is much-needed because to the different and separate nature of the users.
( More boxes and layers) defining a federated system, composed of communities of interest from NCEs (network of centers of excellence). Each COI publishes a service description that is available within the NCE.
The problem in POKM is that it is a single SOAP but multiple different users. So, eg. the animal tracking COI consists of a migration service and a detection service. The detection system meanwhile is based on two databases, POST and OBIS (one for Pacific, one for Atlantic). The idea here is that the detection service would query the repository to determine which database to use.
(Diagrams of scripts for asdding new services - experts need to define mappings)
(Neat diagram of a Yahoo-pipes like composition of multiple services.) The tool being used mostly is Glassfish.
Question: how many man-years did it take to develop something like this. Answer: more than 10.
Interface Between Modular Ontology and Applications
Faezeh Ensan, UNB
OWL-DL has challenges for representing complex ontologies. For example, it has difficulty with efficient reasoning. Also, evolution management is a challenge. Finally, there is an issue with the integration of different developed parts.
Two approaches to address this:
- decompose large ontology into smaller parts
- develop modular formalisms
The purpose of modular ontology formalisms is to make integration and reasoning using the ontology feasible.
The knowledge is 'encapsulated' the knowledge in the ontology is represented through interfaces. So ontology modules can be efficiently interchanged. You can get different perspectives to a module. And it supports polynorphism.
Example: tourism ontology. It could use either 'Canada destinations' or 'North America destinations'. Or, a 'sightseeing' subsystem could link to an ontology related to 'scientific attractions', or to an ontology related to 'natural attractions'.
The knowledge base of the 'utilizer' modules are augmented by sets of nominals from 'realizer' modules. After augmentation, the knowledge engines are not expected to take into account the realizer modules any more. (Diagram makes it look like data from the realizer module is integrated into the utilizer module).
(Screenshop of application)
We applied the modular ontology in Service New Brunswick. We needed to define some sort of common meaning of the terms related to 'research' for SNB, to precisely define research. The modular ontology has modules related to finding, governance, working with existing proceses, and reporting activities.
The main module was 'research project'; other modules were 'reserach funding', 'research performers', 'corporation partners', etc.
(Diagram of 'requirements' module). A requirement is a construct that comes from a concept called a 'requirement', that can be a 'gap', say, or a 'new idea', or 'opportunity', of 'issues', etc. There will be a 'priority', different 'approaches', such as 'development', 'research', each of which will have 'risk' and 'cost'. Etc.
Elements of this ontology can come from other ontologies. For example, the 'requirements' module may have a 'project' or 'SNB Unit'. These are defined by other modules.
(Diagram of Research Project module). A reserach project needs a requirement. This requirement is defined by the requirements module. It needs a funding source, defined by another module. Etc.
(SD - I like the concept of ontology modules.)
(Boxes and layers diagram - query execution.) Kind of a neat example where a 'query dispatcher' breaks a query into two separate queries, one for each module. Then an aggregator unit joins the results from these queries and displays the results.
Question, about whether it would be better to use an meta-ontology. Answer: we can do this without the need for a meta-ontology.
NICHE Research Group
Sajjad Hussain, Dalhousie
(Not on agenda)
Stands for kNowledge Intensive Computing for Healthcare Enterprises. Includes about 20 researchers. Mostly we deal with health care technology. Mostly proof-of-concepts.
(Big boxes and layers diagram) - everything from personal health records to health team collaboration to evidence organization to clinical practice guidelines.
(Even bigger boxes and layers diagram - more boxes than they have people). Everything from policy to risk assessemnt to social networks, etc.
(List of 9 projects).
Sample project: CarePlan - a "rich temporal peocess-specific clinical pathway that manages the evolving dynamics of a patient to meet the patient's needs, institutional workflows and medical knowledge". (Not making this up - SD)
The idea is to use a 'logic-based proof engine' to generate a 'personalized care plan'.
Another project - "knowledge morphing for clinical decision support". 'Knowledge orphing' is defined as the intelligent and autonomous fusion/integtation of contextually, conceptually and functionally related knowledge objects'.
(Nonsense. It's all nonsense. -SD)
(Diagram of 'knowledge morphing' by combining ontology fragments, to eg. generate contextualized subontologies, merge contextualized subontologies, and detect contradictory knowledge in merged knowledge components.)
Another project - structural-semantic matching, done at the proof level. Interesting if you like formal proofs. The proofs are based on 'similarities' (but again these similarities are based on 'distances').
Detecting and Resolving Inconsistencies in Ontologies Using Contradiction Derivations
Sajjad Hussain
Ontologies are the foundational representation formalism for web based information sharing. Ontology engineers develop domain ontologies by either making them from scratch or adapting existing ontologies. During this process, inconsistencies can occur.
Inconsistencies can occur either during original ontology production, or as an effect of an ontology reconciliation project, There is potential harm to the ontology as a result.
Formal definitions (I won't repeat the squiggles here) of 'ontologies (as a description logic 'DL) with axioms, and sub-ontologies that talk about some concept sof the main ontologies. Then we define 'ontology triples', which are created from a logic program defined by a set of rules.
(ick, I have always hated formalism - SD)
Next we define 'constraint rules' and 'contradictory triple sets'. This conjunction of triples leads to a contradiction. This allows us to define an inconsistent ontology (as one containing a contradiction, duh) and maximally consistent sub-ontology (being one such that any addition will produce a contradiction).
Example 'whale is a big fish' contraduiction detection.
(I can't bear to take more notes - he's standing there facing his slides and reading them, and his slides are sets of equations and formulae. "Every one of them.... must have.... triples... each of them.... the triple of Mi.... must have.... at least.... one of them.")
Harold Boley, NRC
Presented sample of RDF being used in BBC music site. Wikipedia is using external, public data sets. In short, it is using the web of data as a CMS. More and more data is on the web, and more and more applications rely on that data.
We ned to break out of database silos. We need the infrastructre for a web of data, so data can be interlinked and integrated on the web, so we can start making queries on the whole, not just specific databases.
Example: book. There may be a dataset with ID, author, tite, ec. and separate records for the author and home page, and publisher and home page.
We could export this data as a set of relations. Some of these resources might not have globally visible URLs, though, like the author and publisher information. Still, the relations form a graph. One can export the data generating relations on the fly, and can export part of the data.
We could take this data and merge it with other data, provided we have the same resource id. (Example of english & french metadata being merged). Eg. Combine the 'person' metadata from the book with other sources, such as Wikipedia data (dbpedia.org).
This can be done automatically. This is where ontologies extra rules, etc., come in. These can be relatively simple and small, or huge, or anything in between. And we can ask even more powerful queries of the web of data.
Semantic Web Services
Omair Sgafiq, University of Calgary
Conceptual Model
Next Generation of the web moves from static to dymamic (WWW to WS) and from syntactic to semantic (WWW to semantic web). The final step is combined: Semantic Web Services
Web services are programs accessible over the web. The service oriented architecture (SOA) uses web services as basic building blocks. The current technology includes WDSL, SOAP, UDDI
The web service usage process involves a discovery process where people find services they need. But consumers can't use the descriptions to find services - the description is syntactic, which means nothibg to the user. Also, there's no semantically marked-up content, no support for the semantic web.
To fix this, people have introduced semantic web services. It uses ontologies as the data model, and invokes web services as an integral part. This gives us 'Semantically Enables Service Oriented Architecture' (SESA). We semantically describe client requests, employ mediation techniques, and semantically enhance the complete SOA lifecycle.
Goal-Driven Web Service Usage
There is currently no layer that describes user objectives formally. This requires that service providers lift up semantic descriptions of their services into the web services discover layer, and the objective, described semntically, can be matched to that.
This leads to a definition of WSMO. It combines:
- Goals: objectives that a client wants to achieve
- Ontologies: formally specified terminology used by all other components
- Web Services - seantic descriptions of capabilities and interface
- Mediators - connectors between components with mediation facilities
Ontologies are the data model used throughout. Ontology elements include concepts, attributes, relations, functions, instances, and axioms. These specify the knowledge domain of the service,
The description of the service includes Dublin Core, versioning, quality of sercvice, and financial information, plus functionality, such as choreography and orchestration. (This is *very* high level and - frankly - looks like vaporware - SD).
The capability specification includes pre-conditions, assumptions, post-conditions and effects. Pre-conditions might require, for example, a departure and destination point. The post-condition might be the issuing of a ticket. Choreography tells the service in what order it should perform steps. Orchestration allows service providers to invoke other service providers.
In the objectives section, the client can formally specify their requests. The prupose is to facilitate problem-oriented WS usage, dynamic WS usage, and to provide a central client element. The goal description has three parts: requested capability, requested choreography, and requested orchestration.
The idea is to accept a goal, and decompose it into services. The client has different goal templates, or can write a complete description. First, selection is carried out, then, in operation, the service is actually invoked.
The mediators ensure heterogenty between the data and the processes. This is needed because, eg., clients will write goals in isolation from sevrice providers, leading to the possibility of mismatch. Mediators use a declarative approach, providing a semantic description of resources. Mediation cannot be fully automated.
(There are different approaches to creating semantically-enabled web services).
Semantic Matching
Virendra C. Bhavsar, UNB
Syntactic matching - is basically exact string matching. The result is 'yes' or 'no' (1 or 0). This also includes matching permutations of strings. For example, you can get a measure of similarity between two strings, by dividing number of words in common, with total number of words in string. But this sort of similarity does not have much meaning. We would match, eg. 'electruc chair' and 'committee chair'.
Semantic matching would be useful in a variety of application, for example, e-business, e-learning, matchmaking portals, web services, etc.
Semantic similarity - is an identity relation, between 0 and 1.
Semantic distance, is a matching relationship, between 1 and infinity. It consists of matching of words, short texts, documents, schemas, taxonomy, etc.
Concept similarity in a taxonomy: we can find the similarity of two nodes in a taxonomy tree by tinking about how 'far' they are, as defined (say) by how many nodes we have to traverse. Or the amount of the path length they have in common. Or you might compare the contents of the nodes. Etc. There are different ways to do this.
The motivation for this is to, eg., match online buyers and sellers in a marketplace. The buyer may define a specific set of requests. You need more than flat representations of resources. You need to be able to identity 'part-of', etc. You also need to be able to match properties of buyers and sellers.
(SD - it seems ridiculous to be talking about the 'similarity' of concepts absent any sort of context - similarity depends entirely on salience - because Bhavsar, by using taxonomy trees to measure similarity, is using a 'distance' measure of similarity he is unable to account for salience - see Amos Tversky, Features of Similarity).
Description of partonomy tree similarity engine (based on eduSource work!). See the teclantic protal http://teclantic,ca which uses this type of similarity matching. (This portal is not active now, however.)
Current work includes weighted semantic tree similarity engines, semantic searching, weighted graph similarity engines, multi-core and cluster implementations, and matchmaking portals.
Resource Description Markup Language
Stephen Downes and Luc Belliveau, NRC
My presentation page is http://www.downes.ca/presentation/256
Using Semantic Web Technologies To Unwind Humanities Citations
Mobertson, MAU.
Discussing how semantic web tecnologies are used to manage citations from texts, eg., Latin texts. How do we cite this work? When you're dealing with Latin literature, there are international standards.
Early citations were non-actionable - they would point to text, but in a different work or volume. Eg. John 3:16 - the advantage, though, is that they will be the same everywhere. John 3:16 will be the same no matter what language (The 'John' but will change, but 3:16 won't.
In the Humanities, we use various abbreviations (eg., 'Ar' = Aristophenes in English, Apristotle in French). These were used just to save print. So we want to expand this, so that it's beautiful and intelligble.
We have, for example, Canonical Text Service URNs. Eg. urb:cts:greekLour:tlg0532:tlg001:1:10
In the browser, we will:
- reject the abbreviation wholly
- exploit dbpedia, etc., to provide internationalized (or partially internationalized) versions of the references
- make references dereferencable - ie., you have the ability to click on the reference and see the text immediately.
To integrate CTS URNs and dbpedia, we are using Swiftowlm. We generate the record. Then we generate HTML 5 data divs to display the information, so we can extract it from the database with SPARQL queries.
It also supports a taxonomy of citation (evidence, refutation, etc). This actually allows the citation inline inside the div.
This will also enable the system to replace old-style scanning systems (JSTOR) with the live cittation system. And we will be able to geo- and temporally reference secondary sources by reference to the primary sources they cite. Etc.
(Described how to extract ontologies from text. turns out the method uses pre-supplied lists of terms).
OWL-based Execution of Clinical Practice Guidelines
Borna Jafarpour, Dalhousie
Clinical practice guidelines (CPGs) have a great deal of information. They support decisions, and place constraints on ordering, among other things. (Sample diagram, with flow of tasks).
The motivation behind OWL-based execution is to provide inference-power of OWL to execute CDGs, to integrate everything together. Thus we can develop a workflow ontology in OWL. We can plug in any reasoner that we want.
Problems in OWL
Handling preconditions: 'any' and 'all' easily handled in OWL. 'any k' preconditions (where k is a number) is harder - you have to use a combination of all and some.
If t2 is a precondition, and in turn has precondition t1, OWL can't handle an uncle problem, This is where the precondition t11 is a subtask of t1, it needs to know t1 satisfies the precondition
Also, another problem is restructions of a subset of data values in a range.
Also, there is the 'open world assumption' - from the absence of a statement, a reasoner should not assume that the statement is false.
Thus there needs to be preprocessing.
(more and more stuff to make it work)
Question: if you have to do all this in OWL, is it worth it?
Platform for Ocean Knowledge Management
Ashraf M. Abusharekh, Dalhousie
We need to mix information from oceanography and marine biology on, say, leatherneck turtles. The leatherneck tuyrtle community of interest has members from both these groups.
We developed an SOA+knowlede management approach that supports multiple knpowledge domains, and multiple disparate and distributed users. This is a framework for publishing new scientific capabilities.
(Yes, the presentations really are this vague)
(Big picture with boxes and layers)
POKM relies mostly on the ocean tracking network (OTN). The POKM ontology is much-needed because to the different and separate nature of the users.
( More boxes and layers) defining a federated system, composed of communities of interest from NCEs (network of centers of excellence). Each COI publishes a service description that is available within the NCE.
The problem in POKM is that it is a single SOAP but multiple different users. So, eg. the animal tracking COI consists of a migration service and a detection service. The detection system meanwhile is based on two databases, POST and OBIS (one for Pacific, one for Atlantic). The idea here is that the detection service would query the repository to determine which database to use.
(Diagrams of scripts for asdding new services - experts need to define mappings)
(Neat diagram of a Yahoo-pipes like composition of multiple services.) The tool being used mostly is Glassfish.
Question: how many man-years did it take to develop something like this. Answer: more than 10.
Interface Between Modular Ontology and Applications
Faezeh Ensan, UNB
OWL-DL has challenges for representing complex ontologies. For example, it has difficulty with efficient reasoning. Also, evolution management is a challenge. Finally, there is an issue with the integration of different developed parts.
Two approaches to address this:
- decompose large ontology into smaller parts
- develop modular formalisms
The purpose of modular ontology formalisms is to make integration and reasoning using the ontology feasible.
The knowledge is 'encapsulated' the knowledge in the ontology is represented through interfaces. So ontology modules can be efficiently interchanged. You can get different perspectives to a module. And it supports polynorphism.
Example: tourism ontology. It could use either 'Canada destinations' or 'North America destinations'. Or, a 'sightseeing' subsystem could link to an ontology related to 'scientific attractions', or to an ontology related to 'natural attractions'.
The knowledge base of the 'utilizer' modules are augmented by sets of nominals from 'realizer' modules. After augmentation, the knowledge engines are not expected to take into account the realizer modules any more. (Diagram makes it look like data from the realizer module is integrated into the utilizer module).
(Screenshop of application)
We applied the modular ontology in Service New Brunswick. We needed to define some sort of common meaning of the terms related to 'research' for SNB, to precisely define research. The modular ontology has modules related to finding, governance, working with existing proceses, and reporting activities.
The main module was 'research project'; other modules were 'reserach funding', 'research performers', 'corporation partners', etc.
(Diagram of 'requirements' module). A requirement is a construct that comes from a concept called a 'requirement', that can be a 'gap', say, or a 'new idea', or 'opportunity', of 'issues', etc. There will be a 'priority', different 'approaches', such as 'development', 'research', each of which will have 'risk' and 'cost'. Etc.
Elements of this ontology can come from other ontologies. For example, the 'requirements' module may have a 'project' or 'SNB Unit'. These are defined by other modules.
(Diagram of Research Project module). A reserach project needs a requirement. This requirement is defined by the requirements module. It needs a funding source, defined by another module. Etc.
(SD - I like the concept of ontology modules.)
(Boxes and layers diagram - query execution.) Kind of a neat example where a 'query dispatcher' breaks a query into two separate queries, one for each module. Then an aggregator unit joins the results from these queries and displays the results.
Question, about whether it would be better to use an meta-ontology. Answer: we can do this without the need for a meta-ontology.
NICHE Research Group
Sajjad Hussain, Dalhousie
(Not on agenda)
Stands for kNowledge Intensive Computing for Healthcare Enterprises. Includes about 20 researchers. Mostly we deal with health care technology. Mostly proof-of-concepts.
(Big boxes and layers diagram) - everything from personal health records to health team collaboration to evidence organization to clinical practice guidelines.
(Even bigger boxes and layers diagram - more boxes than they have people). Everything from policy to risk assessemnt to social networks, etc.
(List of 9 projects).
Sample project: CarePlan - a "rich temporal peocess-specific clinical pathway that manages the evolving dynamics of a patient to meet the patient's needs, institutional workflows and medical knowledge". (Not making this up - SD)
The idea is to use a 'logic-based proof engine' to generate a 'personalized care plan'.
Another project - "knowledge morphing for clinical decision support". 'Knowledge orphing' is defined as the intelligent and autonomous fusion/integtation of contextually, conceptually and functionally related knowledge objects'.
(Nonsense. It's all nonsense. -SD)
(Diagram of 'knowledge morphing' by combining ontology fragments, to eg. generate contextualized subontologies, merge contextualized subontologies, and detect contradictory knowledge in merged knowledge components.)
Another project - structural-semantic matching, done at the proof level. Interesting if you like formal proofs. The proofs are based on 'similarities' (but again these similarities are based on 'distances').
Detecting and Resolving Inconsistencies in Ontologies Using Contradiction Derivations
Sajjad Hussain
Ontologies are the foundational representation formalism for web based information sharing. Ontology engineers develop domain ontologies by either making them from scratch or adapting existing ontologies. During this process, inconsistencies can occur.
Inconsistencies can occur either during original ontology production, or as an effect of an ontology reconciliation project, There is potential harm to the ontology as a result.
Formal definitions (I won't repeat the squiggles here) of 'ontologies (as a description logic 'DL) with axioms, and sub-ontologies that talk about some concept sof the main ontologies. Then we define 'ontology triples', which are created from a logic program defined by a set of rules.
(ick, I have always hated formalism - SD)
Next we define 'constraint rules' and 'contradictory triple sets'. This conjunction of triples leads to a contradiction. This allows us to define an inconsistent ontology (as one containing a contradiction, duh) and maximally consistent sub-ontology (being one such that any addition will produce a contradiction).
Example 'whale is a big fish' contraduiction detection.
(I can't bear to take more notes - he's standing there facing his slides and reading them, and his slides are sets of equations and formulae. "Every one of them.... must have.... triples... each of them.... the triple of Mi.... must have.... at least.... one of them.")
Thursday, 10 June 2010
Submitted to the iDC Mailing List
I haven’t had much to contribute this week because I have been engaged in a couple of projects that will I hope eventually offer open and free access to learning.
- Personal Learning Environment – this project, which is an application and systems development project being undertaken by Canada’s National Research Council, is intended to enable learners easy access to the world’s learning resources from their own personal environment
- Critical Literacies 2010 – this is an open online course, on the model of the Connectivism and Connective Knowledge courses George and I have offered in the past, designed to study and foster the fundamental capacities learners need to flourish in an online environment
For myself, I have little to no interest in ‘trends’ in higher education, nor am I interested in the ‘globalization’ of higher education. Where perhaps once I thought mass movements or mass phenomena were important, these no longer interest me. And where I once thought the needs of learning could be addressed institutionally, I now see institutions playing a smaller and smaller role.
I come to this field originally as a bit of a futurist. I was working as a web developer and instructional designer when I posted ‘The Future of Online Learning’ in 1998. This paper, written originally to explain to my managers what I was working on, caught people’s imagination and, because of its accuracy, had a remarkably long shelf life. A couple of years ago I wrote ‘The Future of Online Learning: Ten Years On’ to update the predictions and draw out some of my thoughts on them.
Today, my work is still very much forward-directed, but I do not (and never have) believe in the inevitability of the future. Yes, we can detect patterns and regularities in events, as I describe in ‘Patterns of Change’, an article I wrote for Critical Literacies last week. But as I state near the end of that article, I believe that choice, decision and selection play a major role in shaping the future.
Thus, while I often think of the future generally, and the future of education in particular, as a gradual migration of mass phenomena to network phenomena, I do not see this progression as inevitable, and indeed, I observe on the part of many quarters efforts to keep us firmly entrenched in the world of mass (I document these and other observations, for those not familiar with it, in an online newsletter, OLDaily ). Change is not only progression, it is also conflict (and it is also cooperation).
So, I don’t care what the majority of educational institutions are doing, I don’t care what the ‘best practices’ are, I don’t care how ‘higher education can make you a better leader’, I don’t even care about debates such as ‘equity or utility’ (sorry George) because these are all things that trade on commonality, general principles, massification, manipulation and control, and ultimately, corporatism and statism (the twin pillars of the mass age).
What I do care about is the personal. This is not some pseudo-Randist individualism, not some sort of Lockean atomism, not a definition of the individual as the granules who, when assembled together, create the commonwealth. I am interested in the person as embedded in society, the person as a member of a network of communications and collaborations, a person who works and creates with and for other people, a person who experiences sociality, but also, and contra the mass nation, a person who is self-governing, guided by his or her own interests and principles, and is living a fully engaged life in a technological civilization.
It is the development of this sort of person that I had in mind when I wrote ‘Things You Really Need to Learn’. I am by no means the first to advocate such an attitude toward education. This is certainly what Illich has in mind in ‘Tools for Conviviality’ :
“The master’s tools will never dismantle the master’s house.” This phrase from Audre Lorde has haunted me ever since I first heard it. The development of, and provision of, tools for the higher education sector, the corporate e-learning sector, or even for the school system, parents, priests or non-profit agencies to use, will never provide the degree of conviviality envisioned by Illich. In these tools there is, and will always be, embedded a dependence back to the originator of the tool, back to the system of mass that makes it both possible and necessary.
I have struggled with the role of the mass in relation to individual freedom and autonomy. I can certainly see the benefit and need of everything to do with mass, from that sense of belonging we all get from being a part of a team to the organized production we require to sustain a modern technological society. I am no myopic idealist looking for the utopian society of perfectly enlightened autonomous individuals working in perfect harmony. But I also write wishing that the mass had some sort of ‘escape’ or ‘no-harm’ clause, or that educators had their own version of the Hippocratic Oath, pledging first, to do no harm.
In the meantime, I work with and for what I believe the internet truly is – an explosion of capacity thrust into the hands of people worldwide, the instrument not only for the greatest outburst of creativity and self-expression ever seen, but also of the greatest autonomy and self-determination, and as well on top of that an unparalleled mechanism for cooperation and cohesion. My view of the internet is as far from the factory as one can imagine. But not as an inevitable or guaranteed future. Only one where there is a determined and directed effort to place the tools – the physical tools, the digital tools, and the cognitive tools – into the hands of a worldwide population, to do with as they will.
I’ve followed the discussions on this list with some interest. But these, too, seem in many respects distant to me. The distinctions of academia, the dialectic of class struggle – these seem to me to miss the essential nature of the change. In the end, to me, the meaning of the internet boils down to a simple utility. One person, one voice. The freedom of each of us to form and to have and to share our own thoughts, created by us, contributed freely to the world, and a society built, not on the basis of a propagation of ideas, but rather, on the basis of a gathering of them.
I haven’t had much to contribute this week because I have been engaged in a couple of projects that will I hope eventually offer open and free access to learning.
- Personal Learning Environment – this project, which is an application and systems development project being undertaken by Canada’s National Research Council, is intended to enable learners easy access to the world’s learning resources from their own personal environment
- Critical Literacies 2010 – this is an open online course, on the model of the Connectivism and Connective Knowledge courses George and I have offered in the past, designed to study and foster the fundamental capacities learners need to flourish in an online environment
For myself, I have little to no interest in ‘trends’ in higher education, nor am I interested in the ‘globalization’ of higher education. Where perhaps once I thought mass movements or mass phenomena were important, these no longer interest me. And where I once thought the needs of learning could be addressed institutionally, I now see institutions playing a smaller and smaller role.
I come to this field originally as a bit of a futurist. I was working as a web developer and instructional designer when I posted ‘The Future of Online Learning’ in 1998. This paper, written originally to explain to my managers what I was working on, caught people’s imagination and, because of its accuracy, had a remarkably long shelf life. A couple of years ago I wrote ‘The Future of Online Learning: Ten Years On’ to update the predictions and draw out some of my thoughts on them.
Today, my work is still very much forward-directed, but I do not (and never have) believe in the inevitability of the future. Yes, we can detect patterns and regularities in events, as I describe in ‘Patterns of Change’, an article I wrote for Critical Literacies last week. But as I state near the end of that article, I believe that choice, decision and selection play a major role in shaping the future.
Thus, while I often think of the future generally, and the future of education in particular, as a gradual migration of mass phenomena to network phenomena, I do not see this progression as inevitable, and indeed, I observe on the part of many quarters efforts to keep us firmly entrenched in the world of mass (I document these and other observations, for those not familiar with it, in an online newsletter, OLDaily ). Change is not only progression, it is also conflict (and it is also cooperation).
So, I don’t care what the majority of educational institutions are doing, I don’t care what the ‘best practices’ are, I don’t care how ‘higher education can make you a better leader’, I don’t even care about debates such as ‘equity or utility’ (sorry George) because these are all things that trade on commonality, general principles, massification, manipulation and control, and ultimately, corporatism and statism (the twin pillars of the mass age).
What I do care about is the personal. This is not some pseudo-Randist individualism, not some sort of Lockean atomism, not a definition of the individual as the granules who, when assembled together, create the commonwealth. I am interested in the person as embedded in society, the person as a member of a network of communications and collaborations, a person who works and creates with and for other people, a person who experiences sociality, but also, and contra the mass nation, a person who is self-governing, guided by his or her own interests and principles, and is living a fully engaged life in a technological civilization.
It is the development of this sort of person that I had in mind when I wrote ‘Things You Really Need to Learn’. I am by no means the first to advocate such an attitude toward education. This is certainly what Illich has in mind in ‘Tools for Conviviality’ :
if we give people tools that guarantee their right to work with high, independent efficiency, thus simultaneously eliminating the need for either slaves or masters and enhancing each person’s range of freedom. People need new tools to work with rather than tools that “work” for them. They need technology to make the most of the energy and imagination each has, rather than more well-programmed energy slaves.So little of what we read or see in the field of online learning is concerned with providing people with the tools they need to create their own freedom. Study the work on e-learning and you will find a preponderance of material addressed to achieving corporate objectives and ROI, advancing the interests of colleges and universities, meeting employment needs and developing industrial strategies, assisting in the privatization or corporatization of the learning infrastructure, extending the reach of a given technology or product network, or subsumption of learning entirely under the individual’s relation as ‘consumer’ with a corporate entity (whether that entity is government or private sector).
“The master’s tools will never dismantle the master’s house.” This phrase from Audre Lorde has haunted me ever since I first heard it. The development of, and provision of, tools for the higher education sector, the corporate e-learning sector, or even for the school system, parents, priests or non-profit agencies to use, will never provide the degree of conviviality envisioned by Illich. In these tools there is, and will always be, embedded a dependence back to the originator of the tool, back to the system of mass that makes it both possible and necessary.
I have struggled with the role of the mass in relation to individual freedom and autonomy. I can certainly see the benefit and need of everything to do with mass, from that sense of belonging we all get from being a part of a team to the organized production we require to sustain a modern technological society. I am no myopic idealist looking for the utopian society of perfectly enlightened autonomous individuals working in perfect harmony. But I also write wishing that the mass had some sort of ‘escape’ or ‘no-harm’ clause, or that educators had their own version of the Hippocratic Oath, pledging first, to do no harm.
In the meantime, I work with and for what I believe the internet truly is – an explosion of capacity thrust into the hands of people worldwide, the instrument not only for the greatest outburst of creativity and self-expression ever seen, but also of the greatest autonomy and self-determination, and as well on top of that an unparalleled mechanism for cooperation and cohesion. My view of the internet is as far from the factory as one can imagine. But not as an inevitable or guaranteed future. Only one where there is a determined and directed effort to place the tools – the physical tools, the digital tools, and the cognitive tools – into the hands of a worldwide population, to do with as they will.
I’ve followed the discussions on this list with some interest. But these, too, seem in many respects distant to me. The distinctions of academia, the dialectic of class struggle – these seem to me to miss the essential nature of the change. In the end, to me, the meaning of the internet boils down to a simple utility. One person, one voice. The freedom of each of us to form and to have and to share our own thoughts, created by us, contributed freely to the world, and a society built, not on the basis of a propagation of ideas, but rather, on the basis of a gathering of them.
Monday, 7 June 2010
Sunday, 6 June 2010
Responding to Greg Breining: Going green? Good luck .. in the Minneapolic Star-Tribune, which was cited by Doug Johnson in an article in Blue Skunk Blog.
Articles such of this one in the Star-Tribune should be widely discredited, not recycled as 'fact'.
The whole "use of water" statistic is misleading. Water that is "used" is not always destroyed; in fact, in usually isn't. Take coffee, for example. A good percentage of the water "used" is water that goes into washing the beans. The water may go down the drain and into the sea, but it isn't destroyed.
This is also the case with e-readers. The water "used" doesn't actually end up inside the e-reader. Otherwise, the reader would be mostly water. Rather, it is water that is used for cleaning, cooling, and other ancillary operations. Sometimes it just drains off; other times it is emitted as steam (whereupon it becomes rain again almost immediately). Or this example, also from the article: "The water used in making a single pair of leather shoes: 4,400 gallons, writes Kostigen." Obviously, shoes do not contain 4,400 gallons of water. Obviously the water is used in washing (and maybe feeding cows and leather plant workers?) and other non-consumptive processes.
Sometimes the advice is ridiculous. "Kostigen also suggested that we each have an obligation to save water because water shortages are common elsewhere." Think about that for a second. How would saving water in Minneapolis - or Ontario - help someone in the Sahara or Australia? It won't, not a whit. Water is so heavy it is almost never transported any great distance; that's why water shortages exist. Water shortages are local conditions, and can only be addressed by saving and production locally. Saving water if you live in a rainforest won't do a thing to help people living in a desert.
Of more concern are the materials used to create ebooks, and in particular, the minerals and the energy. Once again, the figures are misleading. Of the 33 pounds of materials, very few are actually used; the remainder is typically the rock based from which the mineral was extracted. Other materials are catalysts, and may be transformed, but don't cease to exist. Very little actual silicon, aluminum, copper or lithium actually goes into the devices.
(That said, we are almost at 'peak lithium' - but the greater culprit here will be, as usual, cars. As well, the plastics and synthetics used in the devices are often oil-based, but again, the consumption is a small fraction of that consumed in the manufacture and driving of cars).
And while some of these materials are "exotic metals from oppressed and war-torn countries" the problem here is not the ebook readers themselves but rather the conditions under which we extract the materials. Over time, the number of oppressed nations has decreased, which is good. And a great many of these materials are extracted from nations like Canada and Russia, hardly the definition of oppressed and war-torn.
The energy required is probably the most significant. And it's important to notice how the author evades stating the exact truth with phrasing like "an equivalent [of] 100 kilowatt hours of fossil fuel and produces 66 pounds of carbon dioxide."
In fact, very little of the electricity used to produce ebook readers comes from fossil fuel sources. It's too expensive! It costs twice as much for electricity produced using oil and coal plants as it does electricity produced using hydro plants. Hydro plants are clean and use a renewable fuel - the energy of water as it flows from mountains to the see (the water is not "used" - it is still perfectly good after powering the turbines).
To day, less and less electricity is produced using fossil fuels. Most nations are using a combination of hydro, wind and nuclear power. Operations that require a lot of power and water - the extraction of aluminum from bauxite, for example - are located near remote rivers and hydro dams - places like Shawinigan or Kitimat, for example - and don't use any fossil fuels at all and produce only a small fraction of the carbon dioxide suggested by the author.
Finally, let's consider the economics of ebook readers. "An e-reader, said the Times, doesn't break even until it has replaced the production of 40 to 100 books." It is interesting that this quote comes from a newspaper, which produces the equivalent of at least a book every day. If - generously - we equate one newspaper to one book, we reach the break even point in about four months of use. That's assuming the only use was to read one newspaper each day, and nothing else. If fact, with actual use, the break-even point is more like a couple of weeks.
A lot of work goes into minimizing or discrediting the efforts of environmentalists. Articles like this try to p;lay on their purported misconceptions. They are usually arguments created by and for energy end environment wasting industries like newspapers and newsprint. They are afraid of newsprint readers because they reduce demand for what is actually a very expensive product, and provide access to content that now can be distributed around the world almost for free. But what they are really trading on - and perpetuating - is their own readers' lack of knowledge.
Another example in the same article, for example, says: "Near Leamington, Ontario, 1,600 acres -- more than two square miles -- is under glass. Folks nearby can say goodbye to far-off, hard-as-rock California tomatoes in favor of plump local tomatoes all winter long. But the fuel saved in transportation doesn't compare with the energy consumed in lighting the greenhouses in the dark of winter and heating them with propane."
I've been to Leamington and can attest to the scale of the greenhouse operations. But the article misrepresents what is happening. You can't grow enough tomatos for 16 million people in two square kilometers (even though that's an awful lot of tomatos). And, fortunately, the "dark of winter" is very brief, very mild, and very light in southern Ontario (which is at the same latitude as northern California). The greenhouses are used only part of the time. Each greenhouse is surrounded by square kilometers of field. Drive through there in the summer or fall and you'll see field after field of tomato growing outdoors. They have been nurtured from seedlings indoors, but not grown indoors.
And, in fact, it's not clear that even running a local greenhouse full time is more expensive than trucking produce from California. Perhaps while gas and diesel are at their current, artificially low, pump prices, it appears more economical. But realizing, again, that most Ontario power is produced by hydro power (not a small bit is coming from the nearby Niagara Falls) the cost is actually a lot less than the writer might suspect.
It's disappointing and sometimes even dispiriting to see such rubbish printed in what should be a credible source. It's a reminder that we cannot depend on traditional media and traditional sources (such as Discover magazine) for our education. We are well and truly on our own, as most of our established media are now doing more harm than good through their ongoing and pernicious political activism.
Articles such of this one in the Star-Tribune should be widely discredited, not recycled as 'fact'.
The whole "use of water" statistic is misleading. Water that is "used" is not always destroyed; in fact, in usually isn't. Take coffee, for example. A good percentage of the water "used" is water that goes into washing the beans. The water may go down the drain and into the sea, but it isn't destroyed.
This is also the case with e-readers. The water "used" doesn't actually end up inside the e-reader. Otherwise, the reader would be mostly water. Rather, it is water that is used for cleaning, cooling, and other ancillary operations. Sometimes it just drains off; other times it is emitted as steam (whereupon it becomes rain again almost immediately). Or this example, also from the article: "The water used in making a single pair of leather shoes: 4,400 gallons, writes Kostigen." Obviously, shoes do not contain 4,400 gallons of water. Obviously the water is used in washing (and maybe feeding cows and leather plant workers?) and other non-consumptive processes.
Sometimes the advice is ridiculous. "Kostigen also suggested that we each have an obligation to save water because water shortages are common elsewhere." Think about that for a second. How would saving water in Minneapolis - or Ontario - help someone in the Sahara or Australia? It won't, not a whit. Water is so heavy it is almost never transported any great distance; that's why water shortages exist. Water shortages are local conditions, and can only be addressed by saving and production locally. Saving water if you live in a rainforest won't do a thing to help people living in a desert.
Of more concern are the materials used to create ebooks, and in particular, the minerals and the energy. Once again, the figures are misleading. Of the 33 pounds of materials, very few are actually used; the remainder is typically the rock based from which the mineral was extracted. Other materials are catalysts, and may be transformed, but don't cease to exist. Very little actual silicon, aluminum, copper or lithium actually goes into the devices.
(That said, we are almost at 'peak lithium' - but the greater culprit here will be, as usual, cars. As well, the plastics and synthetics used in the devices are often oil-based, but again, the consumption is a small fraction of that consumed in the manufacture and driving of cars).
And while some of these materials are "exotic metals from oppressed and war-torn countries" the problem here is not the ebook readers themselves but rather the conditions under which we extract the materials. Over time, the number of oppressed nations has decreased, which is good. And a great many of these materials are extracted from nations like Canada and Russia, hardly the definition of oppressed and war-torn.
The energy required is probably the most significant. And it's important to notice how the author evades stating the exact truth with phrasing like "an equivalent [of] 100 kilowatt hours of fossil fuel and produces 66 pounds of carbon dioxide."
In fact, very little of the electricity used to produce ebook readers comes from fossil fuel sources. It's too expensive! It costs twice as much for electricity produced using oil and coal plants as it does electricity produced using hydro plants. Hydro plants are clean and use a renewable fuel - the energy of water as it flows from mountains to the see (the water is not "used" - it is still perfectly good after powering the turbines).
To day, less and less electricity is produced using fossil fuels. Most nations are using a combination of hydro, wind and nuclear power. Operations that require a lot of power and water - the extraction of aluminum from bauxite, for example - are located near remote rivers and hydro dams - places like Shawinigan or Kitimat, for example - and don't use any fossil fuels at all and produce only a small fraction of the carbon dioxide suggested by the author.
Finally, let's consider the economics of ebook readers. "An e-reader, said the Times, doesn't break even until it has replaced the production of 40 to 100 books." It is interesting that this quote comes from a newspaper, which produces the equivalent of at least a book every day. If - generously - we equate one newspaper to one book, we reach the break even point in about four months of use. That's assuming the only use was to read one newspaper each day, and nothing else. If fact, with actual use, the break-even point is more like a couple of weeks.
A lot of work goes into minimizing or discrediting the efforts of environmentalists. Articles like this try to p;lay on their purported misconceptions. They are usually arguments created by and for energy end environment wasting industries like newspapers and newsprint. They are afraid of newsprint readers because they reduce demand for what is actually a very expensive product, and provide access to content that now can be distributed around the world almost for free. But what they are really trading on - and perpetuating - is their own readers' lack of knowledge.
Another example in the same article, for example, says: "Near Leamington, Ontario, 1,600 acres -- more than two square miles -- is under glass. Folks nearby can say goodbye to far-off, hard-as-rock California tomatoes in favor of plump local tomatoes all winter long. But the fuel saved in transportation doesn't compare with the energy consumed in lighting the greenhouses in the dark of winter and heating them with propane."
I've been to Leamington and can attest to the scale of the greenhouse operations. But the article misrepresents what is happening. You can't grow enough tomatos for 16 million people in two square kilometers (even though that's an awful lot of tomatos). And, fortunately, the "dark of winter" is very brief, very mild, and very light in southern Ontario (which is at the same latitude as northern California). The greenhouses are used only part of the time. Each greenhouse is surrounded by square kilometers of field. Drive through there in the summer or fall and you'll see field after field of tomato growing outdoors. They have been nurtured from seedlings indoors, but not grown indoors.
And, in fact, it's not clear that even running a local greenhouse full time is more expensive than trucking produce from California. Perhaps while gas and diesel are at their current, artificially low, pump prices, it appears more economical. But realizing, again, that most Ontario power is produced by hydro power (not a small bit is coming from the nearby Niagara Falls) the cost is actually a lot less than the writer might suspect.
It's disappointing and sometimes even dispiriting to see such rubbish printed in what should be a credible source. It's a reminder that we cannot depend on traditional media and traditional sources (such as Discover magazine) for our education. We are well and truly on our own, as most of our established media are now doing more harm than good through their ongoing and pernicious political activism.
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