DMOP’s development was no trivial exercise, however, and several modeling problems popped up that required use of OWL 2 DL features and started to stretch the recent performance improvements of the automated reasoners. A summary of the ontology and a description, discussion, and solution of those issues—or: the choices we made for version 5.3 of the ontology—is described in our OWLED’13 paper Modeling issues and choices in the Data Mining OPtimization Ontology [2], which was co-authored with Agnieszka Lawrynowicz (from uni of Poznan, who will present the paper at OWLED’13), Claudia d’Amato (uni of Bari), and Melanie Hilario (uni of Geneva, Axone, and e-lico coordinator).

The main issues we describe in the paper are about meta-modelling and punning, property chains, aligning DMOP to a foundational ontology, and qualities and attributes (and data properties). The meta-modelling topic arose primarily because of the ontological status of Algorithm: is it a class or an instance, and what are the consequences of modeling it either way? Generally, one would consider an algorithm to be an instance, and it can have zero or more implementations that are also instances. In addition, it can take types of inputs (data mining data sets) and outputs (data mining hypotheses), but one cannot assert an axiom that involves both an instance and a class other than instantiation (which is not applicable for an algorithm’s input and output).  In the end, we settled for OWL 2’s punning feature (for details and arguments, refer to the paper).

There is a brief section about property chains, its issues, and that they were resolved. A detailed description how this was done, as well as a generalization of and theoretical foundation for it, was described in my EKAW’12 paper [3] (there’s an informal introduction in an earlier blog post). There were chains that caused undesirable deductions, which are resolved in v5.3 of DMOP using the tests described in [3]. The chains themselves do not exceed the use of three object properties, i.e., two on the left-hand side of the inclusion, yet some nifty desirable inferences can be made now.

Linking DMOP to a foundational ontology does introduce several modelling issues besides the linking of DMOP classes and properties to the categories and relationship in the chosen foundational ontology. These include whether to import or to extend the foundational ontology (normally: import); whether the whole foundational ontology should be imported or only a relevant section of it (i.e., the need for module extraction); harmonize any expressiveness issues (e.g., the foundational ontology may be too expressive for the purpose of the domain ontology); and what to do with any possible differences in ‘modeling philosophies’ between the two ontologies (e.g., data properties). We ended up importing DOLCE-lite. Linking the data mining classes to DOLCE categories was performed manually, where most of them (like algorithm, software, strategy, task, and optimization problem) were asserted as subclasses of dolce:non-physical-endurant, and their characteristics and parameters are subclasses of dolce:abstract-quality.

A tricky representation issue concerns the ‘attributes’ of entities, such as that each FeatureExtractionAlgorithm has a transformation function that is either linear or non-linear. I’m skipping the arguments here in the blog post (it deserves its own one, and see also the paper), and I jump to the choices we made. Instead of using OWL’s data properties, we went for the ‘foundational ontology way’ of dealing with attributes, where an attribute is not a binary relation between a class and a data type, but an entity itself (subsumed by dolce:quality) that, in turn, is related to a space dolce:region. There is where DOLCE stops, but we needed the data types, so we added a data property hasDataValue from dolce:region to the data type anyType. A section of the ontology is depicted graphically in the next figure.

A section of DMOP with a partial representation of DMOP’s ‘attributes’ (Source: [2]).

A few other choices are briefly mentioned in the paper.

Eventually, these and future improvements to DMOP are expected to pay off in the quality of the meta-miner so that it will compute better optimal workflows.

References

[1] Hilario, M., Nguyen, P., Do, H., Woznica, A., Kalousis, A. Ontology-based meta-mining of knowledge discovery workflows. In: Meta-Learning in Computational Intelligence. Volume 358 of Studies in Computational Intelligence. Springer (2011) 273–315.

[2] Keet, C.M., Lawrynowicz, A., d’Amato, C., Hilario, M. Modeling issues and choices in the Data Mining OPtimisation Ontology. 8th Workshop on OWL: Experiences and Directions (OWLED’13), 26-27 May 2013, Montpellier, France. CEUR-WS vol xx (to appear).

[3] Keet, C.M.. Detecting and Revising Flaws in OWL Object Property Expressions. Proc. of EKAW’12. Springer LNAI vol 7603, pp2 52-266.

To be able to do this in a structured fashion, the foundational ontologies have to be matched somehow, and ideally have to have some software support for this. As early as 2003, this issue as foreseen already and the idea of a “WonderWeb Foundational Ontologies Library” (WFOL) proposed, so that—in the ideal case—different domain ontologies can to commit to different but systematically related (modules of) foundational ontologies [1]. However, the WFOL remained just an idea because it was not clear how to align those foundational ontologies and, at the time of writing, most foundational ontologies were still under active development, OWL was yet to be standardised, and there was scant stable software infrastructure. Within the Semantic Web setting, the solvability of the implementation issues is within reach yet not realised, but their alignment is still to be carried out systematically (beyond the few partial comparisons in the literature).

We’re trying to solve these theoretical and practical shortcomings through the creation of the first such online library of machine-processable, aligned and merged, foundational ontologies: the Repository of Ontologies for MULtiple USes ROMULUS. This version contains alignments, mappings, and merged ontologies for DOLCE, BFO, and GFO and some modularized versions thereof, as a start. It also has a section on logical inconsistencies; i.e., entities that were aligned manually and/or automatically and seemed to refer to the same thing—e.g., a mathematical set, a temporal region—actually turned out not to be (at least from a logical viewpoint) due to other ‘interfering’ axioms in the ontologies. What one should be doing with those, is a separate issue, but at least it is now clear where the matching problems really are down to the nitty-gritty entity-level.

We performed a small experiment on the evaluation of the mappings (thanks to participants from DERI, Net2 funds, and Aidan Hogan), and we would like to have more feedback on the alignments and mappings. It is one thing that we, or some alignment tool, aligned two entities, another that asserting an equivalence ends up logically consistent (hence mapped) or inconsistent, and yet another what you think of the alignments, especially the ontology engineers. You can participate in the evaluation: you will get a small set of a few alignments at a time, and then you decide whether you agree, partially agree, or disagree with it, are unsure about it, or skip it if you have no clue.

Finally, ROMULUS also has a range of other features, such as ontology selection, a high-level comparison, browsing the ontology through WebProtégé, a verbalization of the axioms, and metadata. It is the first online library of machine-processable, modularised, aligned, and merged foundational ontologies around. A poster/demo paper [2] was accepted at the Seventh International Conference on Knowledge Capture (K-CAP’13), and papers describing details are submitted and in the pipeline. In the meantime, if you have comments and/or suggestions, feel free to contact Zubeida or me.

References

[1] Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A. Ontology library. WonderWeb Deliverable D18 (ver. 1.0, 31-12-2003). (2003) http://wonderweb.semanticweb.org.

[2] Khan, Z., Keet, C.M. Toward semantic interoperability with aligned foundational ontologies in ROMULUS. Seventh International Conference on Knowledge Capture (K-CAP’13), ACM proceedings. 23-26 June 2013, Banff, Canada. (accepted as poster &demo with short paper)

Non-fiction

I’ll highlight three books that I think would be worth your time reading, for various reasons.

Delusions of gender—the real science behind sex differences by Cordelia Fine (2010) is a well-researched, solid, attack on neurosexism. She systematically debunks spurious claims about hard-wired biological differences in the brain that are increasingly being used to ‘substantiate’ why female humans supposedly would be cognitively less capable than male humans. For instance, the claims based on  ‘blobology’ with of MRI scans, where both the blobs (indicating an increase on brain activity) are flaky and there are statistically insignificant sample sizes to draw any meaningful conclusions that can be extrapolated to the world population (e.g., typically n is between 7 and 15). With the gendered neuroscience data these days, so argues Fine, we’re at the equivalent of the 19thcentury’s pseudoscience about IQ as ‘inferred’ from measured cranium circumfence.

The tipping point by Malcolm Gladwell (2000). It is an easily readable book about what the components are that make some seemingly insignificant aspect results in a relatively large effect, be they ideas, trends, or social behaviour, which is explained through many examples. There are the ‘rules of epidemics’, key figures in a social network (called connectors, mavens, and salesmen), and there has to be a stickyness factor that makes it stay. Maybe some would categorise it as just an interesting hypothesis because it hasn’t been tested well scientifically and there are only a bunch of references for each chapter, but it is fun to read and it does make one contemplate fads and trends and how they have or have not caught on. I read it over the (brief) holidays, and I found out that it is surely also a great conversation starter.

Affluenza (from influenza + affluence) by Oliver James (2007). I’d categorise it in the same just-an-interesting-hypothesis category as The tipping point. Although it is better researched, there are still many gaps that need to be filled before coming up with a solid theory on the emotional damages of materialism and greed and consumerism, how they come about, what is feeding the emotional distress (exhibited by, among others, depression and anxiety) the bad coping strategies (like addictions), and how to prevent it. Gurr identified the difference between absolute and relative deprivation decades ago and this book is squarely within the relative deprivation, but then implicitly making a further distinction within the relative deprivation between what I’d consider real relative deprivation (e.g., you rent an apartment but feel you should be able to buy a house [to make sure you won’t have to live on the street upon retirement], and buying department store clothes vs. branded clothes that your colleague does) and being psychologically disturbed whilst affluent (e.g., erroneously thinking you really need to buy a bigger second holiday home, have to work harder to earn more money so you can go Christmas shopping in Paris to buy your 100th pair of shoes, buying expensive stuff you really do not need but only because your friend has it). The book is more about the latter version. As typical examples for places with high levels of such materialism & greed and being ‘infected with the affluenza virus’, the USA, UK, and Australia are given, and for examples in the other direction, where there is somehow an absence or only a very limited version of the virus, among others, Denmark and parts of Russia. Even after the 500 pages, I still don’t know for sure what it is why some people do have the virus and some people don’t, other than a bunch of possible candidates. The topic and claims are worth investigating further, however, and James’ message—to ensure mental health, one must pursue one’s needs rather than one’s wants—is well-timed in these years of recession.

Fiction

The hunger games trilogy by Susan Collins (2008-2010). Three brilliant page-turners, of which the first and second ones are the best. Read it if you haven’t done so yet. The movie isn’t a substitute.

The Alchemist by Paulo Coelho (1988). A highly recommendable, sweet, feel-good story.

Eclipse by Richard North Patterson (2009). A fictional story set in Nigeria about the murky business of oil and politics and injustice, inspired by the life of Ken Saro-Wiwa. Not happy readings, but gripping and I hope for the Nigerians that life isn’t as bad as the book portrays it.

Mieses karma by David Safier (2007). A German novel about a careerist women who, upon dying, returns first as an ant (with her human-life memory), and has to work her way up through the animal kingdom (mouse, cow, etc.) through selfless good deeds—or: building up good karma—to reincarnate as human being again and be happy with her husband and child. Overall, it has a serious implicit message, but it is told in a very entertaining, laugh-out-loud, way.

To anticipate second-guesses: 1) yes, I had some brain-candy with fantasy and paranormal stuff about aliens and the magic of Greek gods in a 21st century setting, and a few so-called airport novels, and 2) I have put Jared Diamond’s Collapse back onto the bookshelf after reading about half of it because it was too boring and annoying (unlike his Guns, Germs and Steel), and I can’t remember a thing about The secret life of the English language to write anything useful.

I wrote fewer posts in 2012 than in the previous two years, but I do have the intention to stick to the ‘at least 2 posts/month’ frequency for 2013.

Here’s an excerpt:

4,329 films were submitted to the 2012 Cannes Film Festival. This blog had 16,000 views in 2012. If each view were a film, this blog would power 4 Film Festivals

Click here to see the complete report.

1. Is there a difference between a logical and an ontological reasoning service (be that ‘onto’-logical or ‘extra’-logical)? If so,
   1. Why, and what, then, is an ontological reasoning service?
   2. Are there any that can serve at least as prototypical example of an ontological reasoning service?

There’s still no conclusive answer on either of the questions. So, I present here some data and arguments I had and that I’ve heard so far, and I invite you to have your say on the matter. I will first introduce a few notions, terms, tools, and implicit assumptions informally, then list the three positions and their arguments I am aware of.

Some aspects about standard, non-standard, and ontological reasoning services

Let me first introduce a few ideas informally. Within Description Logics and the Semantic Web, a distinction is made between so-called ‘standard’ and ‘non-standard’ reasoning services. The standard reasoning services—which most of the DL-based reasoners support—are subsumption reasoning, satisfiability, consistency of the knowledge base, instance checking, and instance retrieval (see, e.g., [2,3] for explanations). Non-standard reasoning services include, e.g., glass-box reasoning and computing the least common subsumer, they are typically designed with the aim to facilitate ontology development, and tend to have their own plugin or extension to an existing reasoner. What these standard and non-standard reasoners have in common, is that they all focus on the (subset of first order predicate logic) logical theory only.

Take, on the other hand, OntoClean [4], which assigns meta-properties (such as rigidity and unity) to classes, and then, according to some rules involving those meta-properties, computes the class taxonomy. Those meta-properties are borrowed from Ontology in philosophy and the rules do not use the standard way of computing subsumption (where every instance of the subclass is also an instance of its super class and, thus, practically, the subclass has more or features or has the same features but with more constrained values/ranges). Moreover, OntoClean helps to distinguish between alternative logical formalisations of some piece of knowledge so as to choose the one that is better with respect to the reality we want to represent; e.g., why it is better to have the class Apple that has as quality a color green, versus the option of a class GreenObject that has shape apple-shaped. This being the case, OntoClean may be considered an ontological reasoning service. My SubProS and ProChainS [5] put constraints on OWL object property expressions so as to have safe and good hierarchies of object properties and property chains, based on the same notion of class subsumption, but then applied to role inclusion axioms: the OWL object sub-property (relationship, DL role) must be more constrained than its super-property and the two reasoning services check if that holds. But some of the flawed object property expressions do not cause a logical inconsistency (merely an undesirable deduction), so one might argue that the compatibility services are ontological.

The arguments so far

The descriptions in the previous paragraph contain implicit assumptions about the logical vs ontological reasoning, which I will spell out here. They are a synthesis from mine as well as other people’s voiced opinions about it (the other people being, among others and in alphabetical order, Alessandro Artale, Arina Britz, Giovanni Casini, Enrico Franconi, Aldo Gangemi, Chiara Ghidini, Tommie Meyer, Valentina Presutti, and Michael Uschold). It goes without saying they are my renderings of the arguments, and sometimes I state the things a little more bluntly to make the point.

1. If it is not entailed by the (standard, DL/other logic) reasoning service, then it is something ontological.

Logic is not about the study of the truth, but about the relationship of the truth of one statement and that of another. Effectively, it doesn’t matter what terms you have in the theory’s vocabulary—be this simply A, B, C, etc. or an attempt to represent Apple, Banana, Citrus, etc. conformant to what those entities are in reality—as it uses truth assignments and the usual rules of inference. If you want some reasoning that helps making a distinction between a good and a bad formalisation of what you aim to represent (where both theories are consistent), then that’s not the logician’s business but instead is relegated to the domain of whatever it is that ontologists get excited about. A counter-argument raised to that was that the early logicians were, in fact, concerned with finding a way to formalize reality in the best way; hence, not only syntax and semantics of the logic language, but also the semantics/meaning of the subject domain. A practical counter-example is that both Glimm et al [6] and Welty [7] managed to ‘hack’ OntoClean into OWL and use standard DL reasoners for it to obtain de desired inferences, so, presumably, then even OntoClean cannot be considered an ontological reasoning service after all?

2. Something ‘meta’ like OntoClean can/might be considered really ontological, but SubProS and ProChainS are ‘extra-logical’ and can be embedded like the extra-logical understanding of class subsumption, so they are logical reasoning services (for it is the analogue to class subsumption but then for role inclusion axioms).

This argument has to do with the notion of ‘standard way’ versus ‘alternative approach’ to compute something and the idea of having borrowed something from Ontology recently versus from mathematics and Aristotle somewhat longer ago. (note: the notion of subsumption in computing was still discussed in the 1980s, where the debate got settled in what is now considered the established understanding of class subsumption.) We simply can apply the underlying principles for class-subclass to one for relationships (/object properties/roles). DL/OWL reasoners and the standard view assume that the role box/object property expressions are correct and merely used to compute the class taxonomy only. But why should I assume the role box is fine, even when I know this is not always the case? And why do I have to put up with a classification of some class elsewhere in the taxonomy (or be inconsistent) when the real mistake is in the role box, not the class expression? Differently, some distinction seems to have been drawn between ‘meta’ (second order?), ‘extra’ to indicate the assumptions built into the algorithms/procedures, and ‘other, regular’ like satisfiability checking that we have for all logical theories. Another argument raised was that the ‘meta’ stuff has to do with second order logics, for which there are no good (read: sound and complete) reasoners.

3. Essentially, everything is logical, and services like OntoClean, SubProS, ProChainS can be represented formally with some clearly, precisely, formally, defined inferencing rules, so then there is no ontological reasoning, but there are only logical reasoning services.

This argument made me think of the “logic is everywhere” mug I still have (a goodie from the ICCL 2005 summer school in Dresden). More seriously, though, this argument raises some old philosophical debates whether everything can indeed be formalized, and provided any logic is fine and computation doesn’t matter. Further, it conflates the distinction, if any, between plain logical entailment, the notion of undesirable deductions (e.g., that a CarChassis is-a Perdurant [some kind of a process]), and the modeling choices and preferences (recall the apple with a colour vs. green object that has an apple-shape). But maybe that conflation is fine and there is no real distinction (if so: why?).

In my paper [5] and in the two presentations of it, I had stressed that SubProS and ProChainS were ontological reasoning services, because before that, I had tried but failed to convince logicians of the Type-I position that there’s something useful to those compatibility services and that they ought to be computed (currently, they are mostly not computed by the standard reasoners). Type-II adherents were plentiful at EKAW’12 and some at the OCM workshop. I encountered the most vocal Type-III adherent (mathematician) at the OCM workshop. Then there were the indecisive ones and people who switched and/or became indecisive. At the moment of writing this, I still lean toward Type-II, but I’m open to better arguments.

References

[1] Keet, C.M., Artale, A.: Representing and reasoning over a taxonomy of part-whole relations. Applied Ontology, 2008, 3(1-2), 91–110.

[2] F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, and P. F. Patel-Schneider (Eds). The Description Logics Handbook. Cambridge University Press, 2009.

[3] Pascal Hitzler, Markus Kroetzsch, Sebastian Rudolph. Foundations of Semantic Web Technologies. Chapman & Hall/CRC, 2009,

[4] Guarino, N. and Welty, C. An Overview of OntoClean. In S. Staab, R. Studer (eds.), Handbook on Ontologies, Springer Verlag 2009, pp. 201-220.

[5] Keet, C.M. Detecting and Revising Flaws in OWL Object Property Expressions. Proc. of EKAW’12. Springer LNAI vol 7603, pp2 52-266.

[6] Birte Glimm, Sebastian Rudolph, and Johanna Volker. Integrated metamodeling and diagnosis in OWL 2. In Peter F. Patel-Schneider, Yue Pan, Pascal Hitzler, Peter Mika, Lei Zhang, Jeff Z. Pan, Ian Horrocks, and Birte Glimm, editors, Proceedings of the 9th International Semantic Web Conference, volume 6496 of LNCS, pages 257-272. Springer, November 2010.

[7] Chris Welty. OntOWLclean: cleaning OWL ontologies with OWL. In B. Bennet and C. Fellbaum, editors, Proceedings of Formal Ontologies in Information Systems (FOIS’06), pages 347-359. IOS Press, 2006.

• Other person: “why are you here?”
• Me: Uh?
• Other person: “I mean, work at the university. You can earn so much more money when working in industry.”
• Me: Ahh. Well, I have worked in industry for 3.5 years. It was fine for a while, but not enough…

Then I fill in the dots to a greater or lesser extent, depending on the occasion. Related to answering such questions is Anthony Finkelstein’s “why I do what I do” blogpost: it consists of snapshots of positive aspects and events that made him feel it makes it all worthwhile being a professor in software engineering, which is a nice idea to give small hints toward answering it. Here I compiled some of my ‘snapshots’ of positive aspects, pleasant events, and encouraging feedback that have occurred that make me enjoy my job more than to give into a latent thirst for money and possessions and go back to industry (but note that I reserve the right to change my mind again). In random order:

The excitement when you’re the first person in the whole world who solves some particular problem or discovers something hitherto unknown.

After having covered topics like relational algebra, SQL, and distributed databases in the lectures, a student comments, baffled, “I thought databases was just about playing a bit with MS Access, but there’s so much more to it. It’s really amazing!”

I got to see the Sydney Opera House—wanting to see it since I saw a slide of it in my last year of high school during art classes—right before presenting my paper at a top-ranked conference, and the university paid for the trip to the other end of the world.

“We are pleased to inform you that you paper “xxx” has been accepted for …”

I stumbled upon a paper related to my PhD thesis, stating they use my theory to solve the problem they had.

A fourth-year student emailed me at the end of the course that he’s impressed that I’m a caring lecturer also going beyond what I have to do, and that he has yet to meet someone like me.

Socializing with colleagues from different disciplines, and brainstorming about joining forces to research and devise solutions to fix the major problems in the world.

I traveled to Cuba to, upon invitation, teach a course in my research area to well-prepared and motivated students who were eager to learn. And an extension one of the course’s projects even resulted in a joint paper.

A paper cites one of my papers as if it is the default/standard paper to cite on that topic.

Free access to most of the primary sources of scientific information regardless the discipline.

I can investigate issues that I fancy looking into, and even can earn a living with it.

Seeing students surpassing their own expectations and becoming aware of the capabilities they didn’t think themselves they had but actually do have.

Meeting up with colleagues and having stimulating conversations about pressing problems and known unknowns in our oh-so-relevant sub-sub-sub-field of our discipline, alternated with pub talk on the ‘tales from the trenches’ and nerdy trivia.

I know what the box is made of, what it does, and can make it compute what it should compute.

I travel to different countries and meet many people from all over the world, reconfirming time and again we are all very human, and live in and share this world together.

The focus on the first day of the workshop was principally on the modeling itself, modeling features, and some prospects for reasoning with that represented information and knowledge. I had the honour to start the sessions with the talk of the paper that recently won the best paper award at EKAW’12 on “Detecting and Revising Flaws in OWL Object Property Expressions” [1], which was followed by Zubeida Khan’s talk of our paper at EKAW’12 about ONSET: Automated Foundational Ontology Selection and Explanation [2] that was extended with a brief overview of her MSc thesis on an open ontology repository for foundational ontologies that is near completion. Tahir Khan, who is a visiting PhD student (at UKZN) from Fondazione Bruno Kessler in Trento, gave the third talk within the scope of ontology engineering research. The main part of Tahir’s presentation consisted of an overview of his template-based approach for ontology construction that aims to involve the domain experts in the modeling process of domain ontology development in a more effective way [3]. This was rounded off with a brief overview of one component of this approach, which has to do with being able to select the right DOLCE category when one adds a new class to the ontology and integrating OntoPartS for selecting the appropriate part-whole relation [4] into the template-based approach and its implementation in the MoKi ontology development environment.

There were three talks about representation of and reasoning over defeasible knowledge. Informally, defeasible information representation concerns the ability to represent (and, later, reason over) ‘typical’ or ‘usual’ cases that do have exceptions; e.g., that a human heart is typically positioned left, but in people with sinus inversus, it is positioned on the right-hand side in the chest, and policy rules, such as that, normally, users have access to, say, documents of type x, but black-listed users should be denied access. Giovanni Casini presented recent results on extending the ORM2 conceptual data modeling language with the ability to represent such defeasible information [5], which will be presented also at the Australasian Ontology Workshop in early December. Tommie Meyer focused on the reasoning about it in a Description Logics context ([6] is somewhat related to the talk), whereas Ivan Varzinczak looked at the propositional case with defeasible modalities [7], which will be presented at the TARK’13 conference.

Arina Britz and I also presented fresh-fresh in-submission stage results. Arina gave a presentation about semantic similarities and ‘forgetting’ in propositional logical theories (joint work with Ivan Varzinczak), and I presented a unifying metamodel for UML class diagrams v2.4.1, EER, and ORM2 (joint work with Pablo Fillottrani).

Deshen Moodley gave an overview of the HeAL lab at UKZN and outlined some results from his students Ryan Chrichton (MSc) and Ntsako Maphophe (BSc(honours)). Ryan designed an architecture for software interoperability of health information systems in low-resource settings [8]. Ntsako has developed a web-based ontology development and browsing tool for lightweight ontologies stored in a relational database that was tailored to the use case of a lightweight ontology of software artifacts. Ken Halland presented and discussed his experiences with teaching a distance-learning-based honours-level ontology engineering module at UNISA.

Overall, it was a stimulating and interactive workshop that hopefully can, and will, be repeated next year with an even broader participation than this year’s 16 participants.

References

[1] C. Maria Keet. Detecting and Revising Flaws in OWL Object Property Expressions. Proc. of EKAW’12. Springer LNAI vol 7603, pp2 52-266.

[2] Zubeida Khan and C. Maria Keet. ONSET: Automated Foundational Ontology Selection and Explanation. Proc. of EKAW’12. Springer LNAI vol 7603, pp 237-251.

[3] Tahir Khan. Involving domain experts in ontology construction: a template-based approach. Proc. of ESWC’12 PhD Symposium. 28 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 864-869.

[4] C. Maria Keet, Francis Fernandez-Reyes, and Annette Morales-Gonzalez. Representing mereotopological relations in OWL ontologies with OntoPartS. In: Proc. of ESWC’12, 29-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

[5] Giovanni Casini and Alessandro Mosca. Defeasible reasoning for ORM. In: Proc. of AOW’12. Dec 4, Sydney, Australia

[6] Moodley, K., Meyer, T., Varzinczak, I. A Defeasible Reasoning Approach for Description Logic Ontologies. Proc. of SAICSIT’12. Pretoria.

[7] Arina Britz and Ivan Varzinczak. Defeasible modalities. Proc. of TARK’13, Chennai, India.

[8] Ryan Crichton, Deshendran Moodley, Anban Pillay, Richard Gakuba and Christopher J Seebregts. An Interoperability Architecture for the Health Information Exchange in Rwanda. In Foundations of Health Information Engineering and Systems. 2012.

To answer these questions, I examined how it is implemented in computer science curricula around the world, and the sentiment around teaching it by means of two surveys. The first survey was an examination of curricula and syllabi of computer science degrees around the world on whether, and if so, how, they include ToC. The second one was an online opinion survey (about which I reported before). The paper describing the details [2] has been accepted at the 21st Annual Meeting of the Southern African Association for Research in Mathematics, Science, and Technology Education (SAARMSTE’13), and here I will cherry-pick a few salient outcomes. (or go straight to the answers of the three questions)

Syllabi survey

The 17 traditional and comprehensive universities from South Africa were consulted online on offerings of CS programmes and also 15 universities from continental Europe, 15 from the Anglo-Saxon countries, 6 from Asia, 7 from Africa other than South Africa, and 8 from Latin America. It appeared that 27% of the South African universities—University of KwaZulu-Natal, University of South Africa, University of Western Cape, and the University of Witwatersrand—has ToC in the CS curriculum, compared to 84% elsewhere in the world, which is depicted in Figure 1. The regional difference between Europe (93% inclusion of ToC) and the Anglo-Saxon countries (80%) may be an artefact of the sample size, but it deserves further analysis.

Figure 1. Comparison of ToC in the CS curriculum in South Africa and in other countries around the world (FLAT = formal languages and automata theory, i.e., not including topics such as Turing machines computability and complexity).

The level of detail of the ToC course contents as described in the course syllabi online varied (but see below for better data from the opinion survey), and only 43 universities (of the 59 with data) had sufficient information online regarding timing of ToC in their degree programme. Five universities had it explicitly in the MSc degree programme, with the rest mainly in year 2, 3, or 4 (but see below for better data from the opinion survey), and 5 universities have it spread over 2 or more courses, whereas the rest offers it in a single course (sometimes with further advanced courses covering topics such as probabilistic automata and other complexity classes).

Opinion survey

The opinion survey was quite successful, with a response of n=77. 58 respondents had filled in their affiliation, and they were employed mainly at universities and research institutes: 12 respondents gave an South African academic affiliation and thus the majority of respondents were from around the world, including, among others, the USA, UK, Canada, Germany, Italy, Switzerland, Indonesia, China, Cuba, Brazil, and Argentina. A more detailed characterisation of the respondents, as well as the complete (anonymised) raw question answers with percentages, sorted by question (exported from LimeSurvey) are online at http://www.meteck.org/files/tocsurvey/.

There was a near-unanimous agreement (76 of the 77) that ToC should be in programme, 74 (96% of the answers) have it currently in the programme, and 82% had it in their degree programme when they were a student. Overall, the timing when it is taught in the programme has varied little over the years (see Figure 2). Further, for 90% of the responses, ToC is core in the curriculum, and secure in the programme for 86% (only few reasons were provided for “under threat/other”: that it has been removed from some specialisations but not all (in part due to the computer science vs. information systems tensions), or threatened due to low enrolment numbers).

Figure 2. Comparison between when the survey respondents did ToC in their degree and in which year in the programme it is taught at their university (where applicable).

Regarding the topics that should be part of a ToC course, the following is observed. The survey listed 46 topics and for each one, a possible answer [essential/extended/peripheral/no answer] could be given. The complete list of ToC topics ordered on percent ‘essential’ is shown in Table 1. In short: it is perceived decidedly that formal languages, automata, Turing machines, complexity, computability and decidability themes form part of one coherent offering, although the detail of the sub-topics covered may vary.

Table 1. Ordering of the 46 ToC topics, by calculating the percentage of responses that marked it as ‘essential’ out of the given answers.

Given the plentiful anecdotes, hearsay, and assertions in other articles about teaching ToC concerning difficulties with ToC teaching and learning, the survey also included some questions about that. The data provided by the respondents do substantiate the existence of issues to some extent: 44% of the responses answered that there are no issues and everything runs smoothly, or: a slight majority does, which can be subdivided into 32% ‘causes problems in the academic system each year’ and 24% where ‘management/student affairs has gotten used to the fact there are problems’. Several respondents provided additional information regarding the issues, mentioning low pass rates (n=3), that students struggle because they do not see the usefulness of ToC for their career (n=4), that it also depends on the quality of the teacher (n=2), and low enrolment numbers (n=2). For 45%, the first-time pass rates remain below 60% and with 80% of the respondents, the pass rate remains below 80%. The correlation between pass rate and issues is 0.79 (n is to small to draw any conclusions for the other combinations of pass rates, class sizes, extrapolated course content, and having issues).

Discussion

There is much one can discuss about with respect to the data (and more is included in the paper than I cover here in this blog post). Considering the curriculum analysis first, it can be summarized that ToC in CS is solidly in the programme, is oftentimes taught in a single course, and mostly in 2^nd and 3^rd year of the undergrad CS programme. Interestingly, there is a discrepancy between the ‘essential’ content according to the survey and the newly proposed ACM curriculum guidelines; compare Table 1 with Figure 3.

Figure 3. Proposed CS2013’s ToC topics in the Strawman draft (layout edited). 100% of tier-1 and >80% of tier-2 is core and has to be covered, and an undefined amount of the elective topics to facilitate track-development (no tracks have been defined in the CS2013 draft yet).

Considering the Strawman’s “core” topics, one may question the feasibility of imparting a real understanding of complexity classes P and NP without also touching upon computability and Turing machines. Furthermore, the hours indicated in Figure 3 are meant as minimum hours of fact-to-face lectures (i.e., 8 lessons at a South African university, or at least almost 3 weeks of a standard 16 credit course), which, if this minimum is adhered to, amounts to a very superficial treatment of partial ToC topics. As an aside: my ToC students at UKZN now go through all of it (in accordance with the topics listed in the handbook). Comparing the ‘essentials’ list with the older curriculum guidelines [3, 4], however, one observes that they are much more in agreement.

Quite a bit has changed in the computing arena since the late 1980s, and most notably the specialization and diversification of the field. ToC matters more for CS than other recently recognised specialisations within computing—e.g., software engineering, net-centric computing, information systems, and computational biology—and this diversification is, or has to be, recognised by the curriculum developers [5, 6], which should result in putting more or less weight on the core topics (see [7] for a detailed analysis on sub-disciplines within computing and a proposed weighting of curriculum themes). But recall that the Strawman draft is about (different track within) CS only. The diversification and its effect on computing curricula is noticeable clearly only when one compares it with the Software Engineering curriculum guidelines [8]: these guidelines include only a little bit on finite state machines, grammars, and complexity and computability in the “Data structures and algorithms” and “Discrete structures II” themes. It may be the case that, in praxis, those degree programmes called “computer science” indeed do contain the more fundamental topics, such as ToC (and logic, formal methods etc.), and that other ‘tracks’ actually have been given different names already, hence, would have been filtered out unintentionally a priori in the data collection stage of the curriculum survey.

Concerning issues teaching ToC, on an absolute scale, that 56% faces issues with their ToC courses is substantial, and, conversely, it deserves a comparative analysis to uncover what it is that the other half does so as to not have such issues. Based on the comments in the survey and outside (follow-up emails with survey respondents), there are a few directions: it may help to demonstrate better the applicability of ToC topics in the students’ prospective career, have experienced good teachers, and appropriate preparation in prior courses to increase the pass rates. Further, having issues might be related to the quantity and depth of material covered in a ToC course with respect to nominal course load. The data hints also to another possible explanation: even with a 80-100% pass rate and no low enrolment the ‘gotten used to the issues’ was selected occasionally, and vv., with a 41-60% pass rate that everything runs smoothly, thereby indicating that having issues might also be relative to a particular university culture and expectations of students, academics, and management.

Answers to the questions

Looking again at the questions raised at the start, here are the (short) answers to them:

1. How are ToC topics implemented in CS curricula around the world at present? ToC topics in the actual international curricula are more in line with the older curriculum guidelines of [3, 4] than the more recent versions that put less weight on ToC topics. The timing in the curriculum regarding when to teach ToC remains largely stable and for a majority is scheduled in the 2nd or 3rd year.
2. Are there any country or regional differences? There are country/regional differences, the largest one being that ToC is taught at only 27% of the South African traditional and comprehensive universities versus at 84% of the consulted curricula elsewhere in the world. Even including those SA universities with partial ToC coverage does not make up for the differences with elsewhere in the world or any of the proposed CS curriculum guidelines. Other geographic or language-based differences are not deducible from the data, or: based on the data, region does not matter substantially regarding inclusion of ToC in the CS curriculum, except that the slight difference between Europe and the Anglo-Saxon countries deserves further attention.
3. What is the sentiment around teaching ToC in academia that may influence the former? Opinion on ToC is overwhelmingly in favour of having it in the curriculum, and primarily in the 2nd or 3rd year. Also, a large list of topics is considered to be ‘essential’ to the course, and this list is substantially larger than the recent international curricula Strawman drafts’ core for ToC topics (and more like the Strawman drafts’ total ToC list). Despite noted issues with the course, the voices from the field clearly indicate that ToC is here to stay.

In closing (for now): ToC is solidly in the CS degree programme, and perhaps ought to be introduced more widely in South Africa. And just in case you think something along the line of “well, we have pressing issues to solve in South Africa and no time for follies like doodling DFAs and tinkering with Turing machines”: CS and development of novel and good quality software requires an understanding of ToC topics. For instance, to develop a correct isiZulu grammar checker for text processing software or a parser for natural language processing, scalable image pattern recognition algorithms to monitor wildlife tracks with pictures taken in situ in, say, the Kruger park, an ontology-driven user interface for the Department of Science & Technology’s National Recordal System for indigenous knowledge management, and proper data integration to harmonize and streamline service delivery management, to name but a few application scenarios. Foreigners will not do all this for you (and they have their own problems they want to solve), or only for large consulting fees that otherwise could have been used to, among others, install potable water for the 1.3 million South Africans that don’t have it now, provide them closed toilets, ARV etc.

References

[1] ACM/IEEE Joint Task Force on Computing Curricula. (2012). Computer Science Curricula 2013 Strawman Draft (Feb. 2012). ACM/IEEE.

[2] Keet, C.M. An Assessment of Theory of Computation in Computer Science Curricula. 21st Annual Meeting of the Southern African Association for Research in Mathematics, Science, and Technology Education (SAARMSTE’13). BellVille, South Africa, January 14-17, 2013.

[3] Denning, P.J., Comer, D.E., Gries, D., Mulder, M.C., Tucker, A., Turner, A.J. & Young, P.R. (1989) Computing as a discipline. Communications of the ACM, 32(1), 9-23.

[4] UNESCO-IFIP. (1994). A modular curriculum in computer science. UNESCO and IFIP report ED/94/WS/13. 112p.

[5] Sahimi, M., Roach, S., Cuadros-Vargas, E. & Reed, D. (2012). Computer Science curriculum 2013: reviewing the Strawman report from the ACM/IEEE Task Team. In: Proceedings of the 43rd ACM technical Symposium on Computer Science Education (pp. 3-4). Raleigh, North Carolina, USA, February 29 – March 3, 2012. New York: ACM Conference Proceedings.

[6] Rosenbloom, P.S. (2004). A new framework for computer science and engineering. IEEE Computer, 37(11), 23-28.

[7] ACM/IEEE Joint Task Force on Computing Curricula. (2005). The overview report. ACM, AIS, IEEE-CS, September 30, 2005.

[8] ACM/IEEE Joint Task Force on Computing Curricula. (2004). Software Engineering 2004. ACM, IEEE-CS, August 23, 2004.

The main reasons why ‘successful’ is in the title of this post is that there were several interesting papers, I was (co-)author of two full papers (acceptance rate 15%) of which one won the best paper award, useful feedback on the contents of the papers, it was productive regarding meeting up and conversing about our research and networking, and it was held in Galway. The remainder of this posts briefly outlines some of that; there are Springer LNAI conference proceedings and most presentations have been uploaded on YouTube now.

There were three keynotes. Martin Hepp talked about the difference between ontologies and (more lightweight) web ontologies. Michael Uschold reflected on building the Enterprise Ontology and the lessons learned. Lee Harland provided a lot of information about “practical semantics” for the pharmaceutical industry to improve on the drug discovery process with, a.o., flexible data integration, the new W3C draft of the provenance data model, and quantitative data ontology in the Open PHACTS project.

There were several sessions spread over three whole days, grouped by the following topics: knowledge extraction and enrichment, natural language processing, linked data, ontology engineering and evaluation, social and cognitive aspects of knowledge representation, applications of knowledge engineering, and in-use papers.

Unsurprisingly, I’ll zoom in a bit on the ontology engineering contributions. There were several papers on improving the quality of an ontology. María Poveda-Villalón presented the OntOlogy Pitfall Scanner OOPS! tool that implements the current catalogue of 29 pitfalls [1], where pitfalls may be logical consistency issues or due to modeling or due to human understanding. Given an ontology, OOPS! evaluates it on those pitfalls and reports possible instances, which then can be corrected; e.g., a user defined a property to be the inverse of itself or swapped intersection and union in an expression or missing disjointness axioms. Concerning the latter, Sebastien Ferré’s Advocatus Diaboli—or: “pew! pew!”—may come in helpful as well [2]: it lets one explore the ontology, find “absurd” conjuncts, and add an axiom to exclude that. Or: the aim of the Possible World Explorer is to reduce the amount of possible worlds admitted by the ontology and therewith approximate the intended models better. My own contribution on Detecting and Revising Flaws in OWL Object Property Expressions [3]—which won the best paper award—considers flaws in object property expressions, good and safe role boxes/object property expressions, defines two tests to check for that in an ontology, and provides proposals for how to correct the mistakes (there’s an informal introduction in a previous blog post). In addition to these research contributions on finding and fixing flaws, there was also an in-use paper about that, though then applied to SKOS vocabularies [4], which won the best in-use paper award. It combines guidelines and constraints for SKOS in a new tool Skosify and evaluated 14 SKOS vocabularies and thesauri in some detail, therewith improving those artifacts.

From a modelling/ontology viewpoint, the paper about derived roles [5] was really interesting: although I had thought about the basic temporal dimension of roles before, not in such detail as Mizoguchi and co-authors did. For instance, how should one represent ‘murderer’ or ‘examinee’? There is such thing as an “original role” as we commonly know it, but also a “derived role”, where the meaning of the original role is slightly altered, based on the context of that role; e.g., an examinee not only being an examinee whilst writing the exam, but also when she is studying before the exam, and once one is a murderer during the act of killing, one remains ‘a murderer’ for the remainder of one’s life (though, obviously, not permanently stuck in an act of killing). These derived roles have further, more detailed, specifications, which are summarized in the paper.

Another aspect of foundational ontologies is using them in domain ontology development, and the step prior to that: how to figure out what the ‘best’ foundational ontology is for your project. I co-authored a paper about that with my MSc student Zubeida Khan: ONSET: Automated Foundational Ontology Selection and Explanation [6], which was presented by her and also featured at the demo session where colleagues provided suggestions for more nice features. As mentioned in earlier blogposts (e.g., here), features of foundational ontologies were analysed, as well as criteria for selection of a foundational ontology and needs by existing ontology development projects, which were both used to design a tool, ONSET, that helps with automated selection of a foundational ontology and providing an explanation of the computed selection. Riichiro Mizoguchi—from the YAMATO foundational ontology and who was also attending the conference—has provided the values for the criteria of their foundational ontology in the meantime (thank you!), and you will see an updated ONSET very soon.

Some tools have been evaluated more rigorously than others, and there are a myriad of evaluation approaches. One that stands out by having used the Systems Usability Scale and a funny video during the presentation, is the evaluation of the Live OWL Documentation Environment LODE that automatically generates documentation of your ontology in one HTML page [7]. One that stands out for its interesting results, is the paper about the effect of software-supported collaboration features in the ontology development environment [8]. Marco Rospocher presented the user evaluation done with the MoKi modeling wiki with and without its collaboration features and evaluated their effect on ontology development. The collaborative ontology development went better with such features.

More papers deserve attention here (and I may add them later once I have read the papers), and likewise the mention of other people who attended and of which it was really pleasant to meet them again as well as some fist meeting-in-person after reading several of their papers over the years (among others, and in alphabetical order: Claudia d’Amato, Matthieu d’Aquin, Aldo Gangemi, Chiara Ghidini, Patrick Lambrix, Riichiro Mizoguchi, Marco Rospocher, Mari Carmen Suárez-Figueroa, and Michael Uschold), and to my pleasant surprise, there appear to be ontology enthusiasts in Senegal as well (Gaoussou Camara presented a poster about the use of the infectious diseases ontology).

The next EKAW conference in 2014 will be held in Sweden and I’m looking forward to participating again.

References

(note: I tried to find the freely available versions to link to, where I could not find them, the link points to the Springer page of the EKAW’12 proceedings)

[1] María Poveda-Villalón, Mari Carmen Suárez-Figueroa and Asunción Gómez-Pérez. Validating ontologies with OOPS!. EKAW’12. Springer LNAI vol 7603, pp 267-281.

[2] Sebastien Ferré and Sebastian Rudolph. Advocatus Diaboli – Exploratory enrichment of ontologies with negative constraints. EKAW’12. Springer LNAI vol 7603, pp 42-56.

[3] C. Maria Keet. Detecting and Revising Flaws in OWL Object Property Expressions. EKAW’12. Springer LNAI vol 7603, pp2 52-266.

[4] Osma Suominen and Eero Hyvönen. Improving the quality of SKOS vocabularies with Skosify. EKAW’12. Springer LNAI vol 7603, pp 383-397.

[5] Kouji Kozaki, Yoshinobu Kitamura and Riichiro Mizoguchi. A model of derived roles. EKAW’12. Springer LNAI vol 7603, pp 227-236.

[6] Zubeida Khan and C. Maria Keet. ONSET: Automated Foundational Ontology Selection and Explanation. EKAW’12. Springer LNAI vol 7603, pp 237-251.

[7] Silvio Peroni, David Shotton and Fabio Vitali. The Live OWL Documentation Environment: A tool for the automatic generation of ontology documentation. EKAW’12. Springer LNAI vol 7603, pp 398-412.

[8] Chiara Di Franscescomarino, Chiara Ghidini, and Marco Rospocher. Evaluating wiki-enhanced ontology authoring. EKAW’12. Springer LNAI vol 7603, pp 292-301.

For the curious: all SW2022 papers that will be presented are online on the SW2022 page (scroll down to about half-way on the web page for the programme). I picked out a few that I will summarise and comment on below; my selection is based on topic and/or author(s) and/or curious title, and I am a co-author of one of the papers.

Abraham Bernstein will present the first main paper [1], on the “global brain Semantic Web”, where the Internet is going to serve as the analogue to a brain’s neurons. The ‘global brain’ is used as a metaphor (or revamped old-fashioned AI?) for “distributed interleaved human-machine computation”, or, in fancier, more marketable, terms, now also called “collective intelligence” and “social computing”. In short: put the human in the Semantic Web, both as part of the knowledge provider and as educated user. Bernstein zooms in on the need to be able to manage the “motivational diversity, cognitive diversity, and error diversity” with respect to the possibility of realizing this global brain Semantic Web. Alessandro Oltramari’s vision for a cognitive Semantic Web [2] is quite similar to Bernstein’s one, where the semantic web is tuned to the individual user and “it will be an emergent social network of human and artificial cognitive agents interacting in a hybrid environment, where the distinction between physical and virtual will be superseded by the very nature of the entities populating it, namely knowledge objects and knowledge agents” [2]. Compared to these, our vision of interoperability is somewhat more humble.

Oliver Kutz will present our paper [3] about interoperability among ontologies, to be realized with the Distributed Ontology Language (DOL) that is currently in the process of standardisation at ISO (scheduled to be finalized by 2015). DOL is a metalanguage for distributed ontologies that may be represented in different ontology languages (some of the technical details can be found in a recent paper that won the best paper award at FOIS’12 [4] and a few examples are described in [5]). Overall then, it would be nice if, by 2022, we have solved the interoperability issues not only among data, but also the ‘models’ (ontologies, services descriptions etc.) and, especially, their logic-based representation languages. For instance, being able to seamlessly link knowledge that is represented partially in OWL 2 DL and partially in an ontology represented in Common Logic or leaving an OBO ontology like that yet declare more semantics (e.g., cardinality constraints, property chains) ‘around’ it in a more expressive language for those who need it, and advanced features for modularization, which are all realistic usage scenarios with the DOL. Clearly, all this will need some tool support. Initial tools do exist—Hets for reasoning over heterogeneous ontologies and the Ontohub ontology repository—but more can and will have to be done to realize full interoperability.

The paper on the Semantic Web needs (vision?) for cultural heritage [6] offers nothing I did not already know. South Africa has its own programme in that area—albeit called “indigenous knowledge management”, not “cultural heritage”—and we did our own requirements analysis some time ago already [7, 8]. Our list of requirements lists matches the one by Vavliakis et al., and we have a technology maturity analysis, a set of OWL requirements, and actual use cases from the domain experts and users of the Department of Science & technology’s National Recordal System project for indigenous knowledge management (about which I blogged before). That the topics will receive attention also at SW2022 hopefully increases the chance that those requirements will be investigated further, solved, and realized, which, in turn, will improve the software developed here and, ultimately, the people will benefit from it all.

Mutharaju [9] emphasizes on the need for connectivity, personalization and abstraction. Regarding the latter, he notes that “There would be a need to provide multiple (and higher) levels of abstractions and facilitate drill-down mechanisms.” yey! maybe my work on granularity (among others, [10]) will find its way into implementations after all. Also, Mutharaju thinks that the Semantic Web may be of use for the benefit of the environment (e.g., calculating better traffic flow, using sensor data etc.).

A short paper scheduled for the panel session is entitled “The rise of the verb” [11], which I found a curious title: verbs are taken into account already, where a verb’s ontological foundation is, in the Semantic Web context, represented as an object property in OWL or reified under, say, DOLCE’s Perdurant. Considering the contents of the paper, a more suitable title with respect to the contents could have been “action in the Semantic Web”: the paper’s introduction suggests adding something executable to the semantic web by means of JavaScript but where the instruction is specified at the knowledge level. Heiko Paulheim and Jeff Pan also want some language extensions: they argue in favour of language extensions, so as to be able to handle imprecision/uncertainty in particular [12].

Vander Sande and co-authors present a rather bleak vision of the Semantic Web [13], in that it could endanger humanity. They spend the full 6 pages on highlighting the myriad of dangers and the possible misuses of Semantic Web technologies. Among others: ‘semantic spam’ instead of the dumb variety we have gotten used to, where spammers take advantage of the Linked Open Data cloud and otherwise linked social network data to make the spam look more believable; polluting the LOD cloud through link spoofing; identity theft and provenance manipulation; and the Web of Things for autonomous computerized weaponry. One also could have added a follow-through of the saying that ‘knowledge is power’, where better and scaled-up knowledge management facilitates obtaining more power (and power corrupts, and absolute power corrupts absolutely). All this, in turn, goes back to the philosophical issues regarding responsibility in research, engineering, and technology and whether some field is inherently bad, neutral, or good, or whether the bad pops up only with some application scenarios where the technologies could possibly be used. For the Semantic Web, I think it is only the latter, but you may try to convince me otherwise.

Although I won’t be attending, it’s appreciated that the papers are online already, and I can imagine there will be some lively discussions at the SW2022 workshop.

References

[1] Abraham Bernstein. The Global Brain Semantic Web – Interleaving Human-Machine Knowledge and Computation. SW2022, Boston, Nov 11, 2012.

[2] Alessandro Oltramari. Enabling the cognitive Semantic Web. SW2022, Boston, Nov 11, 2012.

[3] Oliver Kutz, Christoph Lange, Till Mossakowski, C. Maria Keet, Fabian Neuhaus, Michael Grüninger. The Babel of Semantic Web tongues – in search of the Rosetta Stone of interoperability. SW2022, Boston, Nov 11, 2012.

[4] Till Mossakowski, Christoph Lange, Oliver Kutz. Three Semantics for the Core of the Distributed Ontology Language. In Michael Gruninger (Ed.), FOIS 2012: 7th International Conference on Formal Ontology in Information Systems, Graz, Austria.

[5] Christoph Lange, Till Mossakowski, Oliver Kutz, Christian Galinski, Michael Grüninger, Daniel Couto Vale. The Distributed Ontology Language (DOL): Use Cases, Syntax, and Extensibility, Terminology and Knowledge Engineering Conference (TKE’12). Madrid, Spain.

[6] Konstantinos N. Vavliakis, Georgios Th. Karagiannis and Pericles A. Mitkas. Semantic Web in Cultural heritage after 2020. SW2022, Boston, Nov 11, 2012.

[7] Thomas Fogwill, Ronell Alberts, C. Maria Keet. The potential for use of semantic web technologies in IK management systems. IST-Africa Conference 2012. May 9-11, Dar es Salaam, Tanzania.

[8] Ronell Alberts, Thomas Fogwill, C. Maria Keet. Several Required OWL Features for Indigenous Knowledge Management Systems. 7th Workshop on OWL: Experiences and Directions (OWLED 2012). 27-28 May, Heraklion, Crete, Greece. CEUR-WS Vol-849. 12p.

[9] Raghava Mutharaju. How I would like Semantic Web to be, for my children. SW2022, Boston, Nov 11, 2012.

[10] C. Maria Keet. A formal theory of granularity. PhD Thesis, KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy. 2008.

[11] Paul Groth. The rise of the verb. SW2022, Boston, Nov 11, 2012.

[12] Heiko Paulheim and Jeff Z. Pan. Why the Semantic Web should become more imprecise. SW2022, Boston, Nov 11, 2012.

[13] Miel Vander Sande, Sam Coppens, Davy Van Deursen, Erik Mannens and Rik Van De Walle. The terminator’s origins or how the Semantic Web could endanger humanity. SW2022, Boston, Nov 11, 2012.