Posts Tagged ‘ontologies’

Part-whole relations, mereotopology and the OntoPartS tool

Part-whole relations are considered essential in knowledge representation and reasoning and, more practically, in ontology development and conceptual data modelling, especially in the subject domains of biology, medicine, geographic information systems, and manufacturing. In contrast to Ontology that sticks to one type of part-of, the modellers and subject domain experts have come up with a plethora of part-whole relations, some of which are considered real parthood relations and others only meronymic (or: due to imprecise natural language use). For instance, the Foundational Model of Anatomy has 8 basic locative part-whole relations [1], GALEN has come up with 26 part-whole relations [2], and in cognitive science and conceptual data modelling, it hovers around about 6 types [3,4]. They have been structured in a taxonomy of part-whole relations that makes a distinction between mereology and meronomy, transitivity and in- or non-transitivity, and the domain and range of the relationship [5], and some initial usage guidelines were proposed in [6].

But that’s not enough for the complex subject domains and demands on the representation and reasoning over the ontologies. This holds in particular when one has to represent that some things are contained in or located in something else. For instance, the way how Paris and France relate is somehow different from how the euro coin in your wallet relate to each other—the latter being an example of  (spatial) containment, but not structural part of—whereas in other case, the spatial containment of regions of space and the structural parthood of the objects occupying those regions do coincide, e.g., your heart in your body. Or consider representing that Alto Adige/Südtirol is a border province of Italy (bordering Austria), where we have to handle both the notion of administrative entities and connecting geographical regions. That is, handling regions and ‘things’ that occupy those regions (mereotopology).

Being more precise about how the things relate provides nice inferences. Take, e.g., NTPLI as ‘non-tangential proper located in’—a part is located in the whole but not at the boundary of it—and $EnclosedCountry \equiv Country \sqcap \exists NTPLI.Country$, with the following instances in our knowledge base $NTPLI(Lesotho, South Africa)$, $Country(Lesotho)$, and $Country(South Africa)$, then it deduces correctly that $EnclosedCountry(Lesotho)$, whereas with a mere ‘part-of’, we would not have been able to obtain this result.

Besides these examples, there are actual system requirements for, among others, annotating and querying multimedia documents and cartographic maps, such as annotating a photo of a beach where the area of the photo that depicts the sand touches the area that depicts the seawater so that, together with the knowledge that Varadero is a tangential proper part of Cuba, the semantically enhanced system can infer possible locations where the photo has been taken, or, vv., it can propose that the photo may depict a beach scene.

But how to cater for such things?

Let me summarise the three main basic problems that have to be resolved first:

1. There is lack of oversight on plethora of part-whole relations, that include real parthood (mereology) parts with their locations (mereotopology), and other part-whole relations (from meronymy);
2. The challenge to figure out which one to use when;
3. The underspecified representation and reasoning consequences when one has to put up with less expressive languages for which technological infrastructure exists.

We propose to solve that in the following way, which is described in detail in [7] that recently got accepted at the 9th Extended Semantic Web Conference (ESWC’12).

The short answer for the reader who is not interested in all the theory, design, and evaluation, but just wants to model quickly: the OntoPartS tool guides you to choose the most appropriate relation and saves the selection into your OWL file.

Now for a slightly longer answer. First, we extend the taxonomy of part-whole relations of [5] with the novel addition of a taxonomy of formally defined mereotopological relations, which is driven by the KGEMT mereotoplogical theory of Varzi [8], resulting in a taxonomy of 23 part-whole relations—mereological, mereotopological, and meronymic ones—therewith ensuring a solid ontological and logic-based foundation.

Second, some things have to be simplified from the KGEMT theory to make it implementable in OWL, and we describe the design rationale and trade-offs so that OntoPartS can load OWL/OWL2-formalised ontologies, and, if desired, modify the OWL file with the chosen relation. Which OWL species is best suited obviously depends on your individual requirements, but from a representation & reasoning and mereotopology viewpoint, OWL 2 DL and OWL 2 RL seem to fit better than the other ones. (Note: there are papers on DL and representing spatial relations and on DL and parthood, and alternative representation choices are discussed in the paper, yet, as far as we are aware of, none deals with mereotopological relations in OWL or, more generally, in DL.)

Third, there is the ‘how to select’ from the 23 relations. To enable a quick selection of the appropriate relation, we avail of a simplified OWL-ized DOLCE ontology—well, just the taxonomy of categories—for the domain and range restrictions imposed on the part-whole relations and with that, we can let the user take shortcuts compared to a lengthy decision procedure. In this way, we reduced the selection procedure to 0-4 options based on just 2-3 inputs. All of this has been structured neatly in implementation-independent activity diagrams, and subsequently has been implemented; see also the demos, the tool, and the OWL version of the taxonomy of the 23 relations.

Last, we have tested OntoPartS with modellers in controlled experiments and it was shown to improve efficiency and accuracy in modeling of part-whole relations.

As mentioned, further details can be found in [7], Representing mereotopological relations in OWL ontologies with OntoPartS, which I co-authored with Francis Fernández-Reyes, with the Instituto Superior Politécnico “José Antonio Echeverría” (CUJAE), and Annette Morales-González, with the Advanced Technologies Application Center (CENATAV), both located in Cuba (the example on semantic annotation of multimedia with spatial relations comes straight from the image processing research being done at CENATAV). A tidbit of non-scientific information: the first version of the OntoPartS tool was developed as part of the mini-project that Francis, Annette (and Alexis, who is into fish fulltime now) had chosen to carry out for the ontology engineering course I taught at the University of Havana in 2010 (mentioned earlier here and here). For the paper, we added some more theory, minor refinements to the tool, and a user evaluation with several CUJAE and UKZN students and a few FUB colleagues (thanks again for their cooperation and interest). We’ve started work on additional features, so if you have any particular request, drop me a line.

References

1. Mejino, J.L.V., Agoncillo, A.V., Rickard, K.L., Rosse, C.: Representing complexity in part-whole relationships within the foundational model of anatomy. In: Proc. of the AMIA Fall Symposium. pp. 450–454 (2003)
2. http://www.opengalen.org/tutorials/crm/tutorial9.html up to http://www.opengalen.org/tutorials/crm/tutorial16.html/.
3. Winston, M., Chaffin, R., Herrmann, D.: A taxonomy of part-whole relations. Cognitive Science 11(4), 417–444 (1987)
4. Odell, J.: Advanced Object-Oriented Analysis & Design using UML. Cambridge: Cambridge University Press (1998)
5. Keet, C.M., Artale, A.: Representing and reasoning over a taxonomy of part-whole relations. Applied Ontology 3(1-2), 91–110 (2008)
6. Keet, C.M.: Part-whole relations in object-role models. In: Proc. of ORM’06, OTM Workshops 2006. LNCS, vol. 4278, pp. 1116–1127. Springer (2006)
7. Keet, C.M., Fernández Reyes, F.C., Morales-González, A.: Representing mereotopological relations in OWL ontologies with OntoPartS. In Simperl, et al., eds.: Proc. of ESWC’12. LNCS, Springer (2012) 27-31 May 2012, Heraklion, Greece.
8. Varzi, A.: Handbook of Spatial Logics, chap. Spatial reasoning and ontology: parts, wholes, and locations, pp. 945–1038. Berlin Heidelberg: Springer Verlag (2007)

First release of the foundational ONtology SElection Tool ONSET

It is well-known that there are theoretical and practical reasons why using a foundational ontology—such as DOLCE, BFO, GFO, SUMO—improve the quality and interoperability of the domain ontology, which recently also has been shown experimentally. However, it is also known that when one desires to use one, it is difficult to choose which one should be used, and why. Reading all the documentation, becoming familiar with the philosophical underpinnings, looking up what other ontology developers did in similar situation and so on, is a time-consuming task. This bottleneck has now been solved with ONSET.

ONSET, the foundational ONtology SElection Tool, does the hard work for you (download jar file). You answer one or more questions, and it will compute a suggestion based on the answers and your priorities, and it explains why the particular foundational ontology was selected. As usability is important, several “explain” buttons were added, in particular in the “ontology commitments” category. To increase a user’s confidence, ONSET not only simply selects a foundational ontology for you, but also explains why by relating it back to the answers the user chose, and it displays all (if any) request that was not met by the selected ontology. The rather basic main page of ONSET contains an example and links to the various versions of the three ontologies.

Zubeida Khan, a recently graduated (cum laude) BSc honours student I supervised, did most of the work to realise ONSET. She went painstakingly through some 50 publications to extract the features of the ontologies, by considering the ‘selling points’ from the side of the foundational ontology developers, assessing what motivates domain ontology developers of ongoing and completed ontology development projects to choose one over the other, and examined independent characteristics (such as the language in which it is available, modularity). A list was compiled consisting of foundational ontology parameters, and the values were filled in for each ontology (in the current version, they are BFO, DOLCE, and GFO). These values were subsequently verified by the respective foundational ontology developers. Zubeida then implemented it in ONSET (download jar file), following good software design practices and taking into account extensibility of the tool.

While ONSET makes it a lot easier for a domain ontology developer to select a foundational ontology, from the Ontology (philosophy) side of things, it, perhaps, raises more questions than it answers (which deserve attention, but not in this blog post).

Feedback is welcome!

A few notes on ESWC2011 in Heraklion

It’s the end of a interesting and enjoyable ESWC’11 conference in Heraklion, Crete. Compared to other conferences, there were many keynote speeches (and not all of them that much on the Semantic Web, but interesting nevertheless), and, as usual, there were parallel sessions with (unfortunately) many co-scheduled presentations I would have liked to attend. Here follows a few notes on them (which I might update once travelled back to SA, as this is written rather hastily before departure).

Keynotes

Jim Hendler’s talk was entitled “Why the Semantic Web will never work”—with the quotation marks. There have been quite a few people uttering that sentence, but, in Hendler’s review of the past 10 years, we actually have achieved more in some areas than initially anticipated and more than pessimists thought was feasible. For instance, “the semantic web will never scale”: it does, according to Hendler, as demonstrated, e.g., by participants in the billion triple challenge and the growing LOD data cloud. Or the “folksonomies will win” (as opposed to, at least, structured vocabularies): wrong again, mainly because it does not achieve its goal without “social context” and it lacks the crucial aspect of links between entities. However, these achievements are principally in the bottom part of the Semantic Web layer cake and Hendler claims that the “ontology story is still confused”, although OWL is to a large degree “succeeding as a KR standard”. Key challenges for Hendler include: relating linked data to ontologies, the equivalent of a database calculus for linked data, and the need for providing a means for evaluating reasoning with incomplete and possibly inconsistent data. UPDATE (13-6): Hendler’s slides are on slideshare.

Lars Backstrom, data scientist at Facebook, gave a keynote about analyzing FB data and working toward ranking and filtering news feeds by turning it into a classification problem using a set of properties (localization, relation to actor, and others). Interestingly, Backstrom emphasized that FB is moving toward more structured data, which makes it easier to manage and analyse with the algorithms they are developing. If that is a good thing or not is a separate discussion, especially regarding privacy issues, which was the talk of Abe Hsuan about (clearly, this does not hold only for FB but the web in general). According to Hsuan, “Privacy cannot exist on a lawless Semantic Web”. It was good for several after-talk discussions among the attendees, and the last word on how to deal with all this has not been said and done yet. In this context, someone may want to have a look at episode 3 of The virtual revolution documentary about non-free services on the Web, the TED-talk on The filter bubble, or the less recent Database nation book.

Andraz Tori, CTO of Zemanta, gave a keynote describing some background of the ‘writing help’, as offered by WordPress since recently, whilst trying to avoid wrong usage of it and cleaning up the data. As you may have guessed, I have not used that feature yet when writing my blog posts (and do not see the need for it from my perspective). Prasad Kantamneni from Yahoo! Gave an interactive keynote on HCI applied to the effects of different web interfaces for their search engines—and the consequences on revenue, which was lively and interesting. Seemingly ‘silly little things’ like putting the keyword in boldface in the search results makes a big difference on how a user scans through the results (more efficient), likewise auto-completion that in the end make you read more of the results page.

Last, but most certainly not least, Chris Welty gave the conference dinner keynote, which was entertaining. He described some hurdles they had overcome in building ‘Watson’, a sophisticated question answering engine that finds answers to trivia/general knowledge quizzes for the Jeopardy! game that, in the end, did consistently outperform the national human experts on it. The talk was filled with entertaining mistakes they encountered during the development of Watson, and what it required to fix them. The key message was that one cannot go in a linear fashion from natural language to knowledge management, but one has to use a integration of various technologies to make a successful ‘intelligent’ tool.

Sessions and other things

Normally I have a dense section on the papers presented in the session here, but due to the very busy conference schedule and shortage of free online papers before the conference, I did not get around reading all the papers that I would have liked (and I don’t cite papers I have not read, still roughly following my approach to conference blogging). The one on removing redundancy in ontologies presented by Jens Wissmann [1] was quite interesting, in particular for its creative reuse of computing justifications to remove ‘redundant’ axioms, i.e., those which can be derived from other knowledge represented in the ontology anyway. This was computationally costly, so they also developed another algorithm with better performance; details and experimental results can be found in the paper. My own paper [2] on the experiment of the use of foundational ontologies in ontology engineering was well-received, and generated quite some interest, such as on the quality of the foundational ontologies themselves and how the results presented could translate to their particular domain ontology scenario. I may add something on epistemic queries, computing generalizations, matching 4K ontologies in one year, and cross-lingual ontology mappings (provided I find the time to do so in the upcoming days).

The panel session about e- and open- Government was a bit meager and can be summarized as: Linked Open Data (LOD) is good and catching on well but the integration problems still exist, and we need (at least) structured controlled vocabularies to fix it.

I will close with an announcement that Alexander Garcia-Castro brought under my attention: there will be an “Ontologies come of Age in the Semantic Web” workshop co-located with ISWC’11.

References

[1] Stephan Grimm and Jens Wissmann. Elimination of redundancy in ontologies. In: Proceedings of the 8th Extended Semantic Web Conference (ESWC’11). Heraklion, Crete, Greece, 29 May – 2 June 2011. Springer LNCS 6643, 260-274.

[2] Keet, C.M. The use of foundational ontologies in ontology development: an empirical assessment. In: Proceedings of the 8th Extended Semantic Web Conference (ESWC’11). Heraklion, Crete, Greece, 29 May – 2 June 2011. Springer LNCS 6643, 321-335.

Every American is a NamedPizza

Or: verbalizing OWL ontologies still doesn’t really work well.

Ever since we got the multi-lingual verbalization of ORM conceptual data models (restricted FOL theories) working in late 2005 [1]—well: the implementation worked in the DOGMA tool, but the understandability of the output depended on the natural language—I have been following on and off the progress on solutions to the problem. It would be really nice if it all had worked by now, because it is a way for non-logician domain experts to validate the knowledge represented in the ontology and verbalization has been shown to be very useful for domain experts (mainly enterprise) validating (business) knowledge represented in the ORM conceptual data modeling language. (Check out the NORMA tool for the latest fancy implementation, well ahead of OWL verbalization in English Controlled Natural Language).

Some of my students worked on it as an elective ‘mini-project’ topic of the ontology engineering courses I have taught [SWT at FUB, UH, UCI, UKZN]. They have tried to implement it for OWL into Italian and Spanish natural language using a template-based approach with some additional mini-grammar-engine to improve the output, or in English as a competitor to the Manchester syntax. All of them invariable run, to a greater or lesser extent, into the problems discussed in [1], especially when it comes to non-English languages, as English is grammatically challenged. Now, I do not intend to offend people who have English as first language, but English does not have features like gendered articles (just ‘the’ instead of ‘el’ and ‘la’, in Spanish), declensions (still ‘the’ instead of ‘der’ ‘des’, ‘dem’, ‘den’ depending on the proposition, in German), conjunction depending on the nouns (just ‘and’ instead of ‘na’, ‘ne’, ‘no’ that is glued onto the second noun depending on the first letter of that noun, in isiZulu), or subclauses where the verb tense changes by virtue of being in a subclause (in Italian). To sort out such basic matters to generate an understandable pseudo-natural language sentence, a considerable amount of grammar rules and a dictionary have to be added to a template-based approach to make it work.

But let us limit ourselves to English for the moment. Then it is still not trivial. There is a paper comparing the different OWL verbalizers [2], such as Rabbit (ROO) and ACE, which considers issues like how to map, e.g., an AllValuesFrom to “Each…”, “Every…” etc. This is an orthogonal issue to the multi-lingual aspects, and I don’t know how that affects the user’s understanding of the sentences.

I had another look at ACE, as ACE also has a web-interface that accepts OWL/XML files (i.e., OWL 2). I tried it out with the Pizza tutorial ontology, and it generated many intelligible sentences. However, there were also phrases like (i) “Everything that is hasTopping by a Mushroom is something that is a MozzarellaTopping or that is a MushroomTopping or that is a TomatoTopping.”, the (ii) “Every American is a NamedPizza” mentioned in the title of this post, and then there are things like  (iii) “Every DomainConcept that is America or that is England or that is France or that is Germany or that is Italy is a Country”. Example (iii) is not a problem of the verbalizer, but merely an instance of GIGO and the ontology should be corrected.

Examples (i) and (ii) exhibit other problems, though. Regarding (ii), I have noticed that when (novice) ontologists use an ontology development tool, it is a not uncommon practice to not name the entity fully, probably because it is easy for a human reader to fill in the rest from the context; in casu, American is not an adjective to people, but relates to pizza. A more precise name could have avoided such issues (AmericanPizza), or a new solution to ‘context’ can be devised. The weird “is hasTopping by” is due, I think, to the lexicalization of OWL’s ObjectPropertyRange in ACE, which takes the object property, assumes that to be in the infinitive and then puts it in the past participle form (see the Web-ACE page, section 4). So, if the Pizza Ontology developers had chosen not hasTopping but, say, the verb ‘top’, ACE would have changed it into ‘is topped by’. In idea the rule makes sense, but it can be thwarted by the names used in the ontology.

Fliedl and co-authors [3] are trying to resolve just such issues. They propose a rigid naming convention to make it easier to verbalize the ontology. I do not think it is a good proposal, because it is ‘blaming’ the ontologists for failing natural language generation (NLG) systems, and syntactic sugar (verbalization) should not be the guiding principle when adding knowledge to the ontology. Besides, it is not that difficult to add another rule or two to cater for variations, which is probably what will be needed in the near future anyway once ontology reuse and partial imports become more commonplace in ontology engineering.

Power and Third [4] readily admit that verbalizing OWL is “dubious in theory”, but they provide data that it may be “feasible in practice”. The basis of their conclusion lies in the data analysis of about 200 ontologies, which show that the ‘problematic’ cases seldom arise. For instance, OWL’s SubClassOf takes two class expressions, but in praxis it is only used in the format of SubClassOf(C CE) or SubClassOf(C C), idem regarding EquivalentClasses—I think that is probably due to Protégé’s interface—which makes the verbalization easier. They did not actually build a verbalizer, though, but the tables on page 1011 can be of use what to focus on first; e.g., out of the 633,791 axioms, there were only 12 SubDataPropertyOf assertions, whereas SubClassOf(Class,Class) appeared 297,293 times (46.9% of the total) and SubClassOf(Class,ObjectSomeValuesFrom(ObjectProperty,Class)) 158,519 times (25.0%). Why this distribution is the way it is, is another topic.

Going back to the multi-lingual dimension, there is a general problem with OWL ontologies, which is, from a theoretical perspective, addressed more elegantly with OBO ontologies. In OBO, each class has an identifier and the name is just a label. So one could, in principle, amend this by adding labels for each natural language; e.g., have a class “PIZZA:12345″ in the ontology with associated labels “tomato @en”, “pomodoro @it”, “utamatisi @zulu” and so forth, and when verbalizing it in one of those languages, the system picks the right label, compared to the present cumbersome and error-prone way of developing and maintaining an OWL file for each language. Admitted, this has its limitations for terms and verbs that do not have a neat 1:1 translation, but a fully lexicalized ontology should be able to solve this (though does not do so yet).

It is very well possible that I have missed some recent paper that addresses the issues but that I have not come across. At some point in time, we’ll probably will (have to) develop an isiZulu verbalization system, so anyone who has/knows of references that point to (partial) solutions is most welcome to add them in the comments section of the post.

References

[1] M. Jarrar, C.M. Keet, and P. Dongilli. Multilingual verbalization of ORM conceptual models and axiomatized ontologies. STARLab Technical Report, Vrije Universiteit Brussels, Belgium. February 2006.

[2] R. Schwitter, K. Kaljurand, A. Cregan, C. Dolbear, G. Hart. A comparison of three controlled natural languages for OWL 1.1. Proc. of OWLED 2008 DC. Washington, DC, USA, 1-2 April 2008.

[3] Fliedl, G., Kop, C., Vöhringer, J. Guideline based evaluation and verbalization of OWL class and property labels. Data & Knowledge Engineering, 2010, 69: 331-342.

[4] Power, R., Third, A. Expressing OWL axioms by English sentences: dubious in theory, feasible in practice. Coling 2010: Poster Volume, pages 1006–1013,

Beijing, August 2010.

Nontransitive vs. intransitive direct part-whole relations in OWL

Confusing is-a with part-of is known to be a common mistake by novice ontology developers. Each time I taught the ontology engineering course, I had included a session of 1-2 hours to explain some basic aspects of part-whole relations and, lo and behold, none of the participants made that mistake in the labs or mini-projects! One awkward thing did pop-up there and at other occasions, though, which had to do with modelling direct parthood that does not go well at the moment, to say the least, for a plethora of reasons. Inclusion of direct parthood is not without philosophical quarrels, and the more I think of it, the more I dislike the relation, but somehow the issue appears often in the context of part-whole relations in ontologies. The observed underlying modelling issue—representing intransitivity versus nontransitivity—holds for any OWL object property anyway, so I will proceed with the general case with an example about giraffes.

Preliminaries

First of all, to clarify terms in the post’s title: INtransitive means that for all x, y, z, if Rxy and Ryz then Rxz does not hold; formally $\forall x, y, z (R(x,y) \land R(y,z) \rightarrow \neg R(x,z)$ and an option to state this in a Description Logic is to use role chaining: $R \circ R \sqsubseteq \neg R$NONtransitive means that we cannot say either way if the property is transitive or intransitive, i.e., in some cases is may be transitive but not in other occasions. Direct parthood is to be understood as follows: if some part x is a direct part of a y, then there is no other object z such that x is a part of z and z is a part of y; formally, $\forall x,y (dpo(x, y) \equiv \neg \exists z (partof(x,z) \land partof(z,y)))$. If direct parthood is in- or non-transitive is beside the point at this stage, so let us look now at what happens with it in an OWL ontology when one tries to model it one way or another.

The OWL ontology and the reasoner

Given that I used the African Wildlife Ontology as a tutorial ontology earlier and the theme appeals to people, I will use it again here. Depending on what we do with the direct parthood relation in the ontology, Giraffe is, or is not, classified automatically as a subclass of Herbivore. Herbivore is a defined class, equivalent to, in Protégé 4.1 notation, (eats only plant) or (eats only (is-part-of some plant)), and Giraffe is a subclass of both Animal and eats only (leaf or Twig). Leaves are part of a twig, twigs of a branch, and branches of a tree that in turn is a subclass of plant. The is-part-of is, correctly according to mereology, included in the ontology as being transitive. Instead of all the is-part-of and is-proper-part-of between plant parts and plants in the AfricanWildlifeOntology1.owl, we model them using direct-part. AfricanWildlifeOntology4a.owl has direct-part as sister object property to is-part-of, AfricanWildlifeOntology4b.owl has it as sub-object property of is-part-of, and neither ontology has any “characteristics” (relational properties) checked for direct-part. Before running the reasoner to classify the taxonomy, what do you think will happen with our Giraffe in both cases?

In AfricanWildlifeOntology4a.owl, Giraffe is still a mere direct subclass of Animal, whereas with AfricanWildlifeOntology4b.owl, we do obtain the (desired) deduction that Giraffe is a Herbivore. That is, we obtain different results depending on where we put the uncharacterized direct-part object property in the RBox. Why is this so?

By not clicking the checkbox “transitive”, an object property is non­-transitive, but not in-transitive. In fact, we cannot represent explicitly that an object property is intransitive in OWL (see OWL guide and related documents). If we put the object property at the top level (or, as in Protégé 4.1, as immediate subproperty of topObjectProperty), then we obtain the behaviour as if the property were intransitive (and therefore Giraffe is not classified as a subclass of Herbivore). However, the direct-part property is really nontransitive in the ontology. When direct-part is put as subproperty of is-part-of, then it inherits the transitivity characteristic from is-part-of and therefore Giraffe is classified as a Herbivore (because now leaf and Twig are part of plant thanks to the transitivity).

Obviously, it holds for any OWL/OWL2 object property that one cannot assert intransitivity explicitly, that an object property’s characteristics are inherited to its subproperties, and this kind of behaviour of nontransitive object properties depends on where you place it in the RBox—whether you like it or not.

How to go forward?

Direct parthood is called isComponentOf in the componency ontology design pattern and is a subproperty of isPartOf. Its inverse is called haspart_directly in the W3C best practices document on Simple Part-Whole relations [1], and is a subproperty of the transitive haspart. The componency.owl notes that isComponentOf is “hasPart relation without transitivity”, the ODP page’s “intent” of the pattern is that it is intended to “represent (non-transitively) that objects either are proper parts of other objects, or have proper parts”, and the W3C best Practices note that, unlike mereological parthood, it is “not transitive”. Hence, if you include either one in your OWL ontology, you will not obtain the intended behaviour. Therefore, I do not recommend using either suggestion.

Setting aside the W3C’s best practices motivation for inclusion of haspart_directly—easier querying for immediate parts, but for the ontology purist this ought not to be the motivation for its inclusion—it is worth digging a little deeper into the semantics of the direct parthood. Maybe a modeller actually wants to represent collections with their members, like each Fleet has as direct parts more than one Ship, or constitution of objects, like clay is directly part of some vase? In both cases, however, we deal with meronymic part-whole relations, not mereological ones (see [2] and references therein); hence, they should not be subsumed by the mereological part-of relation anyway. They can be modelled as sister properties of the part-of relation and have the intended nontransitive behaviour as in, e.g., the pwrelations.owl ontology with a taxonomy of part-whole relations (that can be imported into the wildlife ontology).

Alternatively, there is always the option to choose a sufficiently expressive non-OWL language to represent the direct parthood and the rest of the subject domain and use one of the many first/second order theorem provers.

References

[1] Alan Rector and Chris Welty. Simple Part-Whole relations in OWL ontologies. W3C Editor’s draft, 11 August 2005.

[2] C. Maria Keet and Alessandro Artale. Representing and Reasoning over a Taxonomy of Part-Whole Relations. Applied Ontology, 2008, 3(1-2): 91-110.

IJMSO paper on dependencies between ontology design parameters online

At the time I wrote the previous post on dependencies between ontology design parameters in June, I was of the understanding that I would not be allowed to put my paper accepted for publication in the International Journal on Metadata, Semantics and Ontologies online on my homepage due to the restrictions mentioned in the small print in the copyright form. But upon having gone through the full printing procedure, Inderscience informed me that “the Author may post a postprint of the Article (defined as the Authors post-peer review,accepted paper submitted for final publication by Inderscience) on the authors personal web pages”.

So, the paper was published in volume 5, issue 4, of IJMSO recently and the nicely formatted version can be downloaded there. For those who do not have a subscription or do not want to pay for the improved layout, the scruffy postprint is now freely available and the complete reference is:

Keet, C.M. Dependencies between Ontology Design Parameters. International Journal of Metadata, Semantics and Ontologies, 2010, 5(4): 265-284. DOI: 10.1504/IJMSO.2010.035550.