## Archive for the ‘Ontologies’ Category

### KCAP13 poster on aligning and mapping foundational ontologies

I announced in an earlier post the realisation of the Repository of Ontologies for MULtiple USes ROMULUS foundational ontology library as part of Zubeida’s MSc thesis, as well as that a very brief overview describing it was accepted as a poster/demo paper [1] at the 7th International Conference on Knowledge Capture (KCAP’13) that will take place next week in Banff, Canada. The ‘sneak preview’ of the poster in jpeg format is included below. To stay in style, it has roughly the same colour scheme as the ontology library.

The poster’s content is slightly updated compared to the contents of the 2-page poster/demo paper: it has more detail on the results obtained with the automated alignments. On reason for that is the limited space of the KCAP paper, another is that a more comprehensive evaluation has been carried out in the meantime. We report on those results in a paper [2] recently accepted at the 5th International Conference on Knowledge Engineering and Ontology Development (KEOD’13). The results of the tools aren’t great when compared to the ‘gold standard’ of manual alignments and mappings, but there are some interesting differences due to—and thanks to—the differences in the algorithms that the tools use. Mere string matching generates false positives and misses ‘semantic [near-]synonyms’ (e.g., site vs. situoid, but missing perdurant/occurrent), and a high reliance on structural similarity causes a tool to miss alignments (compare, e.g., the first subclasses in GFO vs. those in DOLCE). One feature that surely helps to weed out false positives is the cross-check whether an alignment would be logically consistent or not, as LogMap does. That is also what Zubeida did with the complete set of alignments between DOLCE, BFO, and GFO, aided by HermiT and Protégé’s explanation feature.

The KEOD paper describes those ‘trials and tribulations’; or: there are many equivalence alignments that do not map due to a logical inconsistency. They have been analysed on the root cause (mainly: disjointness axioms between higher-level classes), and, where possible, solutions are proposed, such as subsumption instead of equivalence or proposing to make them sibling classes. Two such examples of alignments that do not map are shown graphically in the poster: a faltering temporal region that apparently means something different in each of the ontologies, and necessary-for does not map to generic-dependent due to conflicting domain/range axioms. The full list of alignments, mappings, and logical inconsistencies is now not only browsable on ROMULUS, as announced in the KCAP demo paper, but also searchable.

Having said that, it is probably worthwhile repeating the same caution made in the paper and previous blog post: what should be done with the inconsistencies is a separate issue, but at least now it is known in detail where the matching problems really are, so that we can go to the next level. And some mappings are possible, so some foundational ontology interchangeability is possible (at least from a practical engineering viewpoint).

References

[1] Khan, Z.C., 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. (poster &demo)

[2] Khan, Z.C., Keet, C.M. Addressing issues in foundational ontology mediation. Fifth International Conference on Knowledge Engineering and Ontology Development (KEOD’13). 19-22 September, Vilamoura, Portugal.

### Quantitative results on pitfalls in ontologies

The amount of ontologies in the world is becoming large, and is increasing rapidly in amount and size of the ontologies. Such ontologies are only to some extent developed by ontologists/knowledge engineers and increasingly also by ‘novice modellers’ and domain experts. Does one do better than the other? When is an ontology ‘better’? What is the prevalence of pitfalls—potential errors or problems—in those ontologies? Which aspects are pitfalls?

I can go on with such general questions one would want to see answered, for the answers can help in designing better guidelines, methods, and methodologies, and therewith improving both on teaching ontology engineering and cracking the nut of what the ‘quality’ of an ontology really entails. To be sure, there has been done work in this direction, such as [1,2] for general modeling issues and authoring suggestions, methods [3,4], and structured cataloguing of “antipatterns” [5] and “pitfalls” [6,7]. The pitfall catalogue included 29 types of pitfalls (and growing), of which 21 are implemented in the OntOlogy Pitfall Scanner! (OOPS!). The nice thing of having such an automated pitfall detection tool for OWL ontologies, is that is offers the opportunity to obtain quantitative results on assumed quality of many ontologies (or, at least, on the presence of pitfalls that can be detected automatically), which has been noted to be lacking [8].

As you may have guessed already, we did exactly that—the ‘we’ being Mari Carmen Suárez Figueroa, María Poveda-Villalón, and I— with 406 OWL ontologies and we report the results in a recently accepted paper [9] at the 5th International Conference on Knowledge Engineering and Ontology Development (KEOD’13). We did not seek the answer to everything, but narrowed it down to the following questions and hypotheses (copied from the paper):

1. A. What is the prevalence of each of those pitfalls in existing ontologies?
2. B. To what extent do the pitfalls say something about quality of an ontology?

Question B is refined into two sub-questions:

1. Which anomalies that appear in OWL ontologies are the most common?
2. Are the ontologies developed by experienced developers and/or well-known or mature ontologies ‘better’ in some modelling quality sense than the ontologies developed by novices? This is refined into the following hypotheses:
1. i. The prevalence and average of pitfalls is significantly higher in ontologies developed by novices compared to ontologies deemed established/mature.
2. ii. The kind of pitfalls observed in novices’ ontologies differs significantly from those in well-known or mature ontologies.
3. iii. The statistics on observed pitfalls of a random set of ontologies is closer to those of novices’ ontologies than the well-known or mature ones.
4. iv. There exists a positive correlation between the detected pitfalls and the size or number of particular elements of the ontology.
5. v. There exists a positive correlation between the detected pitfalls and the DL fragment of the OWL ontology.

The set of 406 ontologies we used in trying to answer these questions consists of three subsets, being 362 ontologies that were already scanned by OOPS! in the year until Oct 2012 (it was online available, so this can be considered a set of ‘random’ ontologies), 23 ontologies made by novices (students enrolled in an ontology engineering course), and 21 well-known ontologies that we assumed to be relatively mature (developed by ontologists, used applications, etc.), such as DOLCE, GFO, and GoodRelations. The ‘novices’ and ‘mature’ ontologies were scanned by OOPS! as well and also evaluated manually.

To make a long story short, I’ll go straight to the outcome (the materials & methods, data, statistical and qualitative analysis can be found in the paper and its supplementary material [9]). First, all 21 types of pitfalls that OOPS! scans for have been detected in the full set of 406 ontologies. The most common ones detected in the ontologies are the absence of annotations, declaring object properties but not their domain and range classes, and there are some issues with inverses. To a lesser extent, there are also issues with unconnected ontology elements and declaring a definition that is recursive. Second, with respect to the five hypotheses: the results falsify hypotheses (i), (ii), and (v), partially validate (iv) and validate (iii), where, regarding  (iv), for novices, the number of pitfalls/ontology does relate to the size and complexity of the ontology. Or, to put it bluntly: there are no striking differences between the sets of ‘novices’, ‘random’, and ‘mature’ ontologies, therewith providing a general landscape of pitfalls in ontologies.

This wasn’t quite what we had expected. We analysed the ‘novices’ and ‘mature’ ontologies in detail, and could find a few more candidate pitfalls as well as a few false positives (which, when taken into account, have an equalizing effect). Then, one has to ask whether the assumptions were valid. This we discuss in some detail in section 4 of the paper, and we could come up with several pros and cons. Based on the varied reviewers’ comments, I presume you’ll have your own opinion about it as well. Either way, the pitfall catalogue is being extended and the community may need to come up with a better way of defining what ‘maturity’ and ‘ontology quality’ means and how that relates to the presence of pitfalls in an ontology.

While it is certainly interesting and useful (imho) to have insight in the presence of pitfalls, the data and analysis also shows we need more quantitative data on the notion of ontology quality to better figure out what’s going on, so that it can inform the development of guidelines and methods for ontology development so that ontology development can be pushed from an art further into the realm of solid engineering underpinned with science.

References

[1] Noy, N. and McGuinness, D. (2001). Ontology development 101: A guide to creating your first ontology. TR KSL-01-05, Stanford Knowledge Systems Laboratory.

[2] Rector, A. et al. (2004). OWL pizzas: Practical experience of teaching OWL-DL: Common errors & common patterns. In Proc. of EKAW’04, volume 3257 of LNCS, pages 63–81. Springer.

[3] Keet, C. M. (2012). Detecting and revising flaws in OWL object property expressions. In Proc. of EKAW’12, volume 7603 of LNAI, pages 252–266. Springer.

[4] Guarino, N. and Welty, C. (2009). An overview of OntoClean. In Staab, S. and Studer, R., editors, Handbook on Ontologies, pages 201–220. Springer, 2 edition.

[5] Roussey, C., Corcho, O., and Vilches-Blázquez, L. (2009). A catalogue of OWL ontology antipatterns. In Proc. of K-CAP’09, pages 205–206.

[6] Poveda, M., Suárez-Figueroa, M.C., and Gómez-Pérez, A. (2010). Common pitfalls in ontology development. In Current Topics in Artificial Intelligence, CAEPIA 2009 Selected Papers, volume 5988 of LNAI, pages 91–100. Springer.

[7] Poveda-Villalón, M., Suárez-Figueroa, M. C., and Gómez- Pérez, A. (2012). Validating ontologies with OOPS! In Proc. of EKAW’12, volume 7603 of LNAI, pages 267–281. Springer.

[8] Vrandecic, D. (2009). Ontology evaluation. In Staab, S. and Studer, R., editors, Handbook on Ontologies, pages 293–313. Springer, 2nd edition.

[9] Keet, C.M., Suárez Figueroa, M.C., and Poveda-Villalón, M. (2013) The current landscape of pitfalls in ontologies. International Conference on Knowledge Engineering and Ontology Development (KEOD’13). 19-22 September, Vilamoura, Portugal.

### 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)

### 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 Volker. 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.

### 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.

### A successful EKAW’12 conference

Having returned four days ago from the 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12)—held in a sunny (!) and beautiful Galway from 8-12 October—I have not yet managed to read all the papers I checked off to read, but I don’t want to postpone the usual conference blogpost too much. So here it goes.

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.

### Some ideas about what the Semantic Web will look like in 2022

Research into realizing a vision of the Semantic Web has been ongoing for little over 10 years, and a call has gone out to ponder, daydream, fantasize, think wishfully or with fear about “What will the Semantic Web look like 10 years from now?” (SW2022). A selection of the many ideas will be presented on November 11, 2012, at the SW2022 workshop, held in conjunction with the 11th International Semantic Web Conference (ISWC’12) in Boston, USA.

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.

### Fixing flaws in OWL object property expressions

OWL 2 DL is a very expressive language and, thanks to ontology developers’ persistent requests, has many features for declaring complex object property expressions: object sub-properties, (inverse) functional, disjointness, equivalence, cardinality, (ir)reflexivity, (a)symmetry, transitivity, and role chaining. A downside of this is that with the more one can do, the higher is the chance that flaws in the representation are introduced; hence, an unexpected or undesired classification or inconsistency may actually be due to a mistake in the object property box, not a class axiom. While there are nifty automated reasoners and explanation tools that help with the modeling exercise, the standard reasoning services for OWL ontologies assume that the axioms in the ‘object property box’ are correct and according to the ontologist’s intention. This may not be the case. Take, for instance, the following thee examples, where either the assertion is not according to the intention of the modeller, or the consequence may be undesirable.

• Domain and range flaws; asserting hasParent $\sqsubseteq$ hasMother instead of hasMother $\sqsubseteq$ hasParent in accordance with their domain and range restrictions (i.e., a subsetting mistake—a more detailed example can be found in [1]), or declaring a domain or a range to be an intersection of disjoint classes;
• Property characteristics flaws: e.g., the family-tree.owl (when accessed on 12-3-2012) has hasGrandFather $\sqsubseteq$ hasAncestor and Trans(hasAncestor) so that transitivity unintentionally is passed down the property hierarchy, yet hasGrandFather is really intransitive (but that cannot be asserted in OWL);
• Property chain issues; for instance the chain hasPart $\circ$ hasParticipant $\sqsubseteq$ hasParticipant in the pharmacogenomics ontology [2] that forces the classes in class expressions using these properties—in casu, DrugTreatment and DrugGeneInteraction—to be either processes due to the domain of the hasParticipant object property, or they will be inconsistent.

Unfortunately, reasoner output and explanation features in ontology development environments do not point to the actual modelling flaw in the object property box. This is due to that implemented justification and explanation algorithms [3, 4, 5] consider logical deductions only and that class axioms and assertions about instances take precedence over what ‘ought to be’ concerning object property axioms, so that only instances and classes can move about in the taxonomy. This makes sense from a logic viewpoint, but it is not enough from an ontology quality viewpoint, as an object property inclusion axiom—being the property hierarchies, domain and range axioms to type the property, a property’s characteristics (reflexivity etc.), and property chains—may well be wrong, and this should be found as such, and corrections proposed.

So, we have to look at what type of mistakes can be made in object property expressions, how one can get the modeller to choose the ontologically correct options in the object property box so as to achieve a better quality ontology and, in case of flaws, how to guide the modeller to the root defect from the modeller’s viewpoint, and propose corrections. That is: the need to recognise the flaw, explain it, and to suggest revisions.

To this end, two non-standard reasoning services were defined [6], which has been accepted recently at the 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12): SubProS and ProChainS. The former is an extension to the RBox Compatibility Service for object subproperties by [1] so that it now also handles the object property characteristics in addition to the subsetting-way of asserting object sub-properties and covers the OWL 2 DL features as a minimum. For the latter, a new ontological reasoning service is defined, which checks whether the chain’s properties are compatible by assessing the domain and range axioms of the participating object properties. Both compatibility services exhaustively check all permutations and therewith pinpoint to the root cause of the problem (if any) in the object property box. In addition, if a test fails, one or more proposals are made how best to revise the identified flaw (depending on the flaw, it may include the option to ignore the warning and accept the deduction). Put differently: SubProS and ProChainS can be considered so-called ontological reasoning services, because the ontology does not necessarily contain logical errors in some of the flaws detected, and these two services thus fall in the category of tools that focus on both logic and additional ontology quality criteria, by aiming toward ontological correctness in addition to just a satisfiable logical theory. (on this topic, see also the works on anti-patterns [7] and OntoClean [8]). Hence, it is different from other works on explanation and pinpointing mistakes that concern logical consequences only [3,4,5], and SubProS and ProChainS also propose revisions for the flaws.

SubProS and ProChainS were evaluated (manually) with several ontologies, including BioTop and the DMOP, which demonstrate that the proposed ontological reasoning services indeed did isolate flaws and could propose useful corrections, which have been incorporated in the latest revisions of the ontologies.

Theoretical details, the definition of the two services, as well as detailed evaluation and explanation going through the steps can be found in the EKAW’12 paper [6], which I’ll present some time between 8 and 12 October in Galway, Ireland. The next phase is to implement an efficient algorithm and make a user-friendly GUI that assists with revising the flaws.

References

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

[2] Dumontier, M., Villanueva-Rosales, N.: Modeling life science knowledge with OWL 1.1. In: Fourth International Workshop OWL: Experiences and Directions 2008 (OWLED 2008 DC). (2008) Washington, DC (metro), 1-2 April 2008

[3] Horridge, M., Parsia, B., Sattler, U.: Laconic and precise justifications in OWL. In: Proceedings of the 7th International Semantic Web Conference (ISWC 2008). Volume 5318 of LNCS., Springer (2008)

[4] Parsia, B., Sirin, E., Kalyanpur, A.: Debugging OWL ontologies. In: Proceedings of the World Wide Web Conference (WWW 2005). (2005) May 10-14, 2005, Chiba, Japan.

[5] Kalyanpur, A., Parsia, B., Sirin, E., Grau, B.: Repairing unsatisfiable concepts in OWL ontologies. In: Proceedings of ESWC’06. Springer LNCS (2006)

[6] Keet, C.M. Detecting and Revising Flaws in OWL Object Property Expressions. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12), Oct 8-12, Galway, Ireland. Springer, LNAI, 15p. (in press)

[7] Roussey, C., Corcho, O., Vilches-Blazquez, L.: A catalogue of OWL ontology antipatterns. In: Proceedings of K-CAP’09. (2009) 205–206

[8] Guarino, N., Welty, C.: An overview of OntoClean. In Staab, S., Studer, R., eds.: Handbook on ontologies. Springer Verlag (2004) 151–159

### A new version of ONSET and more technical details are now available

After the first release of the foundational ONtology Selection and Explanation Tool ONSET half a year ago, we—Zubeida Khan and I—continued its development by adding SUMO, conducting a user evaluation, and we wrote a paper about it, which was recently accepted [1] at the 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12).

There are theoretical and practical reasons why using a foundational ontology improves the quality and interoperability of the domain ontology, be this by means of reusing DOLCE, BFO, GFO, SUMO, YAMATO, or another one, in part or in whole (see, e.g., [2,3] for some motivations). But as a domain ontology developer, and those who are potentially interested in using a foundational ontology in particular, do ask: which one of them would be best to use for the task at hand? That is not an easy question to answer, and hitherto required from a developer to pore over all the documentation, weighing the pros and cons for the scenario, make an informed decision, know exactly why, and be able to communicate that. This bottleneck has been solved with the ONSET tool. Or, at least: we claim it does, and the user evaluation supports this claim.

In short, ONSET, the foundational ONtology Selection and Explanation Tool helps the domain ontology developer in this task. Upon answering one or more questions and, optionally, adding any scaling to indicate some criteria are more important to you than others, it computes the most suitable foundational ontology for that scenario and explains why this is so, including reporting any conflicting answers (if applicable). The questions themselves are divided into five different categories—Ontology, representation language, software engineering properties, applications, and subject domain—and there are “explain” buttons to clarify terms that may not be immediately clear to the domain ontology developer. (There are a few screenshots at the end of this post.)

Behind the scenes is a detailed comparison of the features of DOLCE, BFO, GFO, and SUMO, and an efficient algorithm. The latter and the main interesting aspects of the former are included in the paper; the complete set of criteria is available in a file on the ONSET webpage. You can play with ONSET using your real or a fictitious ontology development scenario after downloading the jar file. If you don’t have a scenario and can’t come up with one: try one of the scenarios we used for the user evaluation (also online). The user evaluation consisted of 5 scenarios/problems that the 18 participants had to solve, half of them used ONSET and half of them did not. On average, the ‘accuracy’ (computed from selecting the appropriate foundatinal ontology and explaining why) was 3 times higher for those who used ONSET compared to those who did not. The ONSET users also did it slightly faster.

Thus, ONSET greatly facilitates in selecting a foundational ontology. However, I concede that from the Ontology (philosophy) viewpoint, the real research component is, perhaps, only beginning. Among others, what is the real effect of the differences between those foundational ontolgoies for ontology development, if any? Is one category of criteria, or individual criterion, always deemed more important than others? Is there one or more ‘typical’ combination of criteria, and if so, is there a single particular foundational ontology suitable, and if not, where/why are the current ones insufficient? In the case of conflicts, which criteria do they typically involve? ONSET clearly can be a useful aid investigating these questions, but answering them is left to future works. Either way, ONSET contributes to taking a scientific approach to comparing and using a foundational ontology in ontology development, and provides the hard arguments why.

We’d be happy to hear your feedback on ONSET, be this on the tool itself or when you have used it for a domain ontology development project. Also, the tool is very easy to extend thanks to the way it is programmed, so if you have your own pet foundational ontology that is not yet included in the tool, you may like to provide us with the values for the criteria so that we can include it.

Here are a few screenshots: of the start page, questions and an explanation, other questions, and the result (of a fictitious example):

Startpage of ONSET, where you select inclusion of additional questions that don’t make any difference right now, and where you can apply scaling to the five categories.

Section of the questions about ontological commitments and a pop-up screen once the related “Explain” button is clicked.

Another tab with questions. In this case, the user selected “yes” to modularity, upon which the tool expanded the question so that a way of modularisation can be selected.

Section of the results tab, after having clicked “calculate results” (in this case, of a fictitious scenario). Conflicting results, if any, will be shown here as well, and upon scrolling down, relevant literature is shown.

References

[1] Khan, Z., Keet, C.M. ONSET: Automated Foundational Ontology Selection and Explanation. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12). Oct 8-12, Galway, Ireland. Springer, LNAI, 15p. (accepted)

[2] Keet, C.M. The use of foundational ontologies in ontology development: an empirical assessment. 8th Extended Semantic Web Conference (ESWC’11), G. Antoniou et al (Eds.), Heraklion, Crete, Greece, 29 May-2 June, 2011. Springer, Lecture Notes in Computer Science LNCS 6643, 321-335.

[3] Borgo, S., Lesmo, L. The attractiveness of foundational ontologies in industry. In: Proc. of FOMI’08, Amsterdam, The Netherlands, IOS Press (2008), 1-9.