Modelling issues and choices in the development of the Data Mining OPtimization ontology

The Data Mining OPtimization ontology (DMOP) is a sizeable ontology with about 600 classes, over 1000 subclass axioms, more than 100 object properties, 40 object sub-property axioms and about 10 property chains, and thus uses several SROIQ/OWL 2DL features. The ontology contains detailed knowledge represented about data mining tasks, algorithms, hypotheses (mined models or patterns), workflows, and data with its characteristics. Such detailed knowledge is required to meet its high-level aim: to support informed decision-making in the knowledge discovery process. While the ontology can be used as a reference by data miners, its primary purpose—at least, the main motivation why it was developed—is automation of algorithm and model selection that relies heavily on semantic meta-mining [1] (ontology-based meta-analysis where data mining experiments are conducted, annotated, and mined and analysed, and from that patterns are extracted about data mining performance). Unlike other data mining ontologies, DMOP helps proposing not just any set of valid workflows, but optimal workflows, thanks to all this detailed knowledge about data mining. (DMOP was developed in the EU FP7 e-lico project and is used in such a system that proposes relatively optimal workflows.)

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]).

For instance, a ModelingAlgorithm has as quality exactly one LearningPolicy (so, LearningPolicy is a subclass of dolce:quality), this LearningPolicy has as quale exactly one abstract region Eager-Lazy, and that Eager-Lazy has as data value at most one anyType data type to record the value of the learning policy of a modeling algorithm. Although this is more cumbersome than with data properties, it makes the ontology much more reusable for a broader set of application scenarios. This comprehensive approach required quite some modeling effort: there are more than 40 DMOP classes made subclass of dolce:abstract-region, and Characteristic (with its 94 subclasses) and Parameter (with 42 subclasses) are subclasses of dolce:abstract-quality, and most are used in class expressions.

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.

Release of the (beta version of the) foundational ontology library ROMULUS

With the increase on ontology development and networked ontologies, both good ontology development and ontology matching for ontology linking and integration are becoming a more pressing issue. Many contributions have been proposed in these areas. One of the ideas to tackle both—supposedly in one fell swoop—is the use of a foundational ontology. A foundational ontology aims to (i) serve as a building block in ontology development by providing the developer with guidance how to model the entities in a domain, and  (ii) serve as a common top-level when integrating different domain ontologies, so that one can identify which entities are equivalent according to their classification in the foundational ontology. Over the years, several foundational ontologies have been developed, such as DOLCE, BFO, GFO, SUMO, and YAMATO, which have been used in domain ontology development. The problem that has arisen now, is how to link domain ontologies that are mapped to different foundational ontologies?

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)

A few more book suggestions

Following last year’s post on books I read, here’s a selection of the books I read in 2012. They cover more general topics than last year’s focus on (South)(ern) Africa, as the main material on Africa I read last year was on current affairs (rather than background information), with daily newspapers and the hardcopies monthly magazines, such as The Africa Report, New African, and The Thinker.

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.

2012 in review (WP blog stats summary)

The WordPress.com stats helper monkeys prepared a 2012 annual report for this blog (see link below). The amount of visits is still impressive for the kind of blog this is, and feedback is on the increase. Hereby a big thank you to you all for visiting my blog and taking the effort to respond (both visibly online as well as the offline comments I received)!

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.

Logical and ontological reasoning services?

The SubProS and ProChainS compatibility services for OWL ontologies to check for good and ‘safe’ OWL object property expression [5] may be considered ontological reasoning services by some, but according others, they are/ought to be plain logical reasoning services. I discussed this issue with Alessandro Artale back in 2007 when we came up with the RBox Compatibility service [1]—which, in the end, we called an ontological reasoning service—and it came up again during EKAW’12 and the Ontologies and Conceptual Modelling Workshop (OCM) in Pretoria in November. Moreover, in all three settings, the conversation was generalized to the following questions:

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.

My snapshots for why I do what I do

A type of conversation that occurs not infrequently goes alike:

• 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.

Ontologies and conceptual modelling workshop in Pretoria

A first attempt was made in South Africa to get researchers and students together who are interested in, and work on, ontologies, conceptual data modelling, and the interaction between the two, shaped in the form of an interactive Workshop on Ontologies and Conceptual Modelling on 15-16 Nov 2012 in Tshwane/Pretoria (part of the Forum on AI Research (FAIR’12) activities). The participants came from, the University of KwaZulu-Natal, University of South Africa, Fondazione Bruno Kessler, and different research units of CSIR-Meraka (where the workshop was organized and held), and the remainder of the post contains a brief summary of the ongoing and recently competed research that was presented at the workshop.

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.