On that “shared” conceptualization and other definitions of an ontology

It’s a topic that never failed to generate a discussion on all 10 instalments of the ontology engineering course I taught from BSc(hons) up to participants studying toward or already having a PhD: those pesky definitions of what an ontology is. To top it off, like I didn’t know, I also got a snarky reviewer’s comment about it on my Stuff ontology paper [1]:

A comment that might be superficial but I cannot help: since an ontology is usually (in Borst’s terms) assumed to be a ‘shared’ conceptualization, I find a little surprising for such a complex model to have been designed by a sole author. While I acknowledge the huge amount of literature carefully analyzed, it still seems that the concrete modeling decisions eventually relied on the background of a single ontologist

Is that bad? Does that make the Stuff Ontology a ‘nontology’? And, by the by, what about all those loner philosophers who write single-author papers on ontology; should that whole field be discarded because most of the ontology insights were “shared” only from paper submission and publication?

Anyway, let’s start from the beginning. There’s the much-criticized definition of an ontology from Gruber that, it seems, only novices seem to keep quoting (to my irritation, indeed):

An ontology is a specification of a conceptualization. [2]

If you wonder why quite a bit has been written about it: try to answer what “specification” really means and how it is specified, and what exactly a “conceptualization” is. The real fun starts with Borst et al.’s [3] and then Studer et al.’s [4] refinement of Gruber’s version, which the reviewer quoted above alluded to:

An ontology is a formal, explicit specification of a shared conceptualization. [4]

At least there’s the “formal” (be it in the sense of logic or formal ontology), and “explicit”, so something is being made explicit and precise. But “shared”? Shared with whom? How? Is a logical theory that not one, but two, people write down an ontology, then? Or one person develops an ontology and then emails it to a few colleagues or puts it online in, say, the open BioPortal ontology repository. Does that count as “shared” then? Or is it only “shared” if at least one other person agrees with it as is (all reviewers of the Stuff Ontology did, btw), or perhaps (most or all of) the ‘conceptualization’ of it but a few axioms would need a bit of tweaking and cleaning up? Do you need at least a group of people to develop an ontology, and if so, how large should that group be, and should that group consist of independent sub-groups that adopt the ontology (and if so, how many endorsers)? Is a lightweight low-hanging-fruit ontology that is used by a large company a real or successful ontology, but a highly axiomatised ontology with a high tangledness that is used by a specialist organization, not? And even if you canvass and get a large group and/or organization to buy into that formal explicit specification, what if they are all wrong on the reality is supposed to represent? Does it still count as an ontology no matter how wrong the conceptualization is, just because it’s formal, explicit, and shared? Is a tailor-made module of, say, the DOLCE ontology not also an ontology, even if the module was made by one person and made available in an online repository like ROMULUS?

Perhaps one shouldn’t start top-down, but bottom-up: take some things and decide (who?) whether it is an ontology or not. Case one: the taxonomy of part-whole relations is a mini-ontology, and although at the start only ‘shared’ with my co-author and published in the Applied Ontology journal [5], it has been used by quite a few researchers for various (and unintended) purposes afterward, notably in NLP (e.g., [6]). An ontology? If so, since when? Case two: Noy et al. converted the representation of the NCI thesaurus into OWL DL [7]. Does changing the serialisation of a multi-authored thesaurus from one format into another make it an ontology? (more on that below.) Case three: a group of 5 people try to represent the subject domain of, say, breast cancer, but it is replete with mistakes both regarding the reality it ought to represent and unintended modelling errors (such as confusing is-a with part-of). Is it still an ontology, albeit a bad one?

It gets more muddled when the representation language is thrown in (as with case 2 above). What if the ontology turns out to be unsatisfiable? From a logic viewpoint, it’s not a theory then (a consistent set of sentences, is), but if it’s formal, explicit, and shared, is it acceptable that those people who developed the artefact simply have an inconsistent conceptualization and that it still counts as an ontology?

Horrocks et al. [8] simplify the whole thing by eliminating the ‘shared’ aspect:

an ontology being equivalent to a Description Logic knowledge base. [8]

However, this generates a set of questions and problems of its own that are practically also problematic. For instance: 1) whether transforming a UML Class Diagram into OWL ‘magically’ makes it an ontology (answer: no); 2) The NCI Thesaurus to OWL (answer: no); or 3) if you used, say, Common Logic to represent it, that then it could not be an ontology because it’s not formalised in Description Logics (answer: it sure can be one).

There are more attempts to give a definition or a description, notably by Nicola Guarino in [9] (a key paper in the field):

An ontology is a logical theory accounting for the intended meaning of a formal vocabulary, i.e. its ontological commitment to a particular conceptualization of the world. The intended models of a logical language using such a vocabulary are constrained by its ontological commitment. An ontology indirectly reflects this commitment (and the underlying conceptualization) by approximating these intended models. [9]

That’s a mouthful, but at least no “shared” in there, either. And, finally, among the many definitions in [10], here’s Barry Smith and cs.’s take on it:

An ONTOLOGY is a representational artifact, comprising a taxonomy as proper part, whose representational units are intended to designate some combination of universals, defined classes, and certain relations between them. [10]

And again, no “shared” either in this definition. Of course, also with Smith’s definition, there are things one can debate about and pose it against Guarino’s definition, like the “universals” vs. “conceptualization” etc., but that’s a story for another time.

So, to sum up: there is that problem on how to interpret “shared”, which is untenable, and one just as well can pick a definition of an ontology from a widely cited paper that doesn’t include that in the definition.

That said, all this doesn’t help my students to grapple with the notion of ‘an ontology’. Examples help, and it would be good if someone, or, say, the International Association for Ontology and its Applications (IAOA) would have a list of “exemplar ontologies” sooner rather than later. (Yes, I have a list, but it still needs to be annotated better). Another aspect that helps explaining it comes is from Guarino’s slides on going “from logical to ontological level” and on good and bad ontologies. This first screenshot (taken from my slides—easier to find) shows there’s “something more” to an ontology than just the logic, with a hint to reasons why (note to my students: more about that later in the course). The second screenshot shows that, yes, we can have the good, bad, and ugly: the yellow oval denotes the intended models (what it should be), and the other ovals denote the various approximations that one may have tried to represent in an ontology. For instance, representing ‘each human has exactly one brain’ is more precise (“good”) than stating ‘each human has at least one brain’ (“less good”) or not saying anything at all about it an ontology of human anatomy (“bad”), and even “worse” it would be if that ontology ware to state ‘each human has exactly two tails’.

logicontogoddbaduglyonto

Maybe we can’t do better than ‘intuition’ or ‘very wieldy explanation’. If this were a local installation of WordPress, I’d have added a poll on definitions and the subjectivity on the shared-ness factor (though knowing well that science isn’t governed as a democracy). In lieu of that: comments, preferences for one definition or the other, or any better suggestions for definitions are most welcome! (The next instalment of my Ontology Engineering course will start in a few week’s time.)

 

References

[1] Keet, C.M. A core ontology of macroscopic stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI vol. 8876, 209-224.

[2] Gruber, T. R. A translation approach to portable ontology specifications. Knowledge Acquisition, 1993, 5(2):199-220.

[3] Borst, W.N., Akkermans, J.M. Engineering Ontologies. International Journal of Human-Computer Studies, 1997, 46(2-3):365-406.

[4] Studer, R., Benjamins, R., and Fensel, D. Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 1998, 25(1-2):161-198.

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

[6] Tandon, N., Hariman, C., Urbani, J., Rohrbach, A., Rohrbach, M., Weikum, G.: Commonsense in parts: Mining part-whole relations from the web and image tags. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16). pp. 243-250. AAAI Press (2016)

[7] Noy, N.F., de Coronado, S., Solbrig, H., Fragoso, G., Hartel, F.W., Musen, M. Representing the NCI Thesaurus in OWL DL: Modeling tools help modeling languages. Applied Ontology, 2008, 3(3):173-190.

[8] Horrocks, I., Patel-Schneider, P. F., and van Harmelen, F. From SHIQ and RDF to OWL: The making of a web ontology language. Journal of Web Semantics, 2003, 1(1):7.

[9] Guarino, N. (1998). Formal ontology and information systems. In Guarino, N., editor, Proceedings of Formal Ontology in Information Systems (FOIS’98), Frontiers in Artificial intelligence and Applications, pages 3-15. Amsterdam: IOS Press.

[10] Smith, B., Kusnierczyk, W., Schober, D., Ceusters, W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. KR-MED 2006 “Biomedical Ontology in Action”. November 8, 2006, Baltimore, Maryland, USA.

Relations with roles / verbalising object properties in isiZulu

The narratives can be very different for the paper “A model for verbalising relations with roles in multiple languages” that was recently accepted paper at the 20th International Conference on Knowledge Engineering and Knowledge management (EKAW’16), for the paper makes a nice smoothie of the three ingredients of language, logic, and ontology. The natural language part zooms in on isiZulu as use case (possibly losing some ontologist or logician readers), then there are the logics about mapping the Description Logic DLR’s role components with OWL (lose possible interest of the natural language researchers), and a bit of philosophy (and lose most people…). It solves some thorny issues when trying to verbalise complicated verbs that we need for knowledge-to-text natural language generation in isiZulu and some other languages (e.g., German). And it solves the matching of logic-based representations popularised in mainly UML and ORM (that typically uses a logic in the DLR family of Description Logic languages) with the more commonly used OWL. The latter is even implemented as a Protégé plugin.

Let me start with some use-cases that cause problems that need to be solved. It is well-known that natural language renderings of ontologies facilitate communication with domain experts who are expected to model and validate the represented knowledge. This is doable for English, with ACE in the lead, but it isn’t for grammatically richer languages. There, there are complications, such as conjugation of verbs, an article that may be dependent on the preposition, or a preposition may modify the noun. For instance, works for, made by, located in, and is part of are quite common names for object properties in ontologies. They all do have a dependent preposition, however, there are different verb tenses, and the latter has a copulative and noun rather than just a verb. All that goes into the object properties name in an ‘English-based ontology’ and does not really have to be processed further in ontology verbalisation other than beautification. Not so in multiple other languages. For instance, the ‘in’ of located in ends up as affixes to the noun representing the object that the other object is located in. Like, imvilophu ‘envelope’ and emvilophini ‘in the envelope’ (locative underlined). Even something straightforward like a property eats can end up having to be conjugated differently depending on who’s eating: when a human eats, it is udla in isiZulu, but for, say, a dog, it is idla (modification underlined), which is driven by the system of noun classes, of which there are 17 in isiZulu. Many more examples illustrating different issues are described in the paper. To make a long story short, there are gradations in complicating effects, from no effect where a preposition can be squeezed in with the verb in naming an OP, to phonological conditioning, to modifying the article of the noun to modifying the noun. A ‘3rd pers. sg.’ may thus be context-dependent, and notions of prepositions may modify the verb or the noun or the article of the noun, or both. For a setting other than English ontologies (e.g., Greek, German, Lithuanian), a preposition may belong neither to the verb nor to the noun, but instead to the role that the object plays in the relation described by the verb in the sentence. For instance, one obtains yomuntu, rather than the basic noun umuntu, if it plays the role of the whole in a part-whole relation like in ‘heart is part of a human’ (inhliziyo iyingxenye yomuntu).

The question then becomes how to handle such a representation that also has to include roles? This is quite common in conceptual data modelling languages and in the DLR family of DL languages, which is known in ontology as positionalism [2]. Bumping up the role to an element in the representation language—thus, in addition to the relationship—enables one to attach information to it, like whether there is a (deep) preposition associated with it, the tense, or the case. Such role-based annotations can then be used to generate the right element, like einen Betrieb ‘some company’ to adjust the article for the case it goes with in German, or ya+umuntu=yomuntu ‘of a human’, modifying the noun in the object position in the sentence.

To get this working properly, with a solid theoretical foundation, we reused a part of the conceptual modelling languages’ metamodel [3] to create a language model for such annotations, in particular regarding the attributes of the classes in the metamodel. On its own, however, it is rather isolated and not immediately useful for ontologies that we set out to be in need of verbalising. To this end, it links to the ‘OWL way of representing relations’ (ontologically: the so-called standard view), and we separate out the logic-based representation from the readings that one can generate with the structured representation of the knowledge. All in all, the simplified high-level model looks like the picture below.

Simplified diagram in UML Class Diagram notation of the main components (see paper for attributes), linking a section of the metamodel (orange; positionalist commitment) to predicates (green; standard view) and their verbalisation (yellow). (Source: [1])

Simplified diagram in UML Class Diagram notation of the main components (see paper for attributes), linking a section of the metamodel (orange; positionalist commitment) to predicates (green; standard view) and their verbalisation (yellow). (Source: [1])

That much for the conceptual part; more details are described in the paper.

Just a fluffy colourful diagram isn’t enough for a solid implementation, however. To this end, we mapped one of the logics that adhere to positionalism to one of the standard view, being DLR [4] and OWL, respectively. It equally well could have been done for other pairs of languages (e.g., with Common Logic), but these two are more popular in terms of theory and tools.

Having the conceptual and logical foundations in place, we did implement it to see whether it actually can be done and to check whether the theory was sufficient. The Protégé plugin is called iMPALA—it could be an abbreviation for ‘Model for Positionalism And Language Annotation’—that both writes all the non-OWL annotations in a separate XML file and takes care of the renderings in Protégé. It works; yay. Specifically, it handles the interaction between the OWL file, the positionalist elements, and the annotations/attributes, plus the additional feature that one can add new linguistic annotation properties, so as to cater for extensibility. Here are a few screenshots:

OWL’s arbeitetFuer ‘works for’ is linked to the relationship arbeiten.

OWL’s arbeitetFuer ‘works for’ is linked to the relationship arbeiten.

The prey role in the axiom of the impala being eaten by the ibhubesi.

The prey role in the axiom of the impala being eaten by the ibhubesi.

 Annotations of the prey role itself, which is a role in the relationship ukudla.

Annotations of the prey role itself, which is a role in the relationship ukudla.

We did test it a bit, from just the regular feature testing to the African Wildlife ontology that was translated into isiZulu (spoken in South Africa) and a people and pets ontology in ciShona (spoken in Zimbabwe). These details are available in the online supplementary material.

The next step is to tie it all together, being the verbalisation patterns for isiZulu [5,6] and the OWL ontologies to generate full sentences, correctly. This is set to happen soon (provided all the protests don’t mess up the planning too much). If you want to know more details that are not, or not clearly, in the paper, then please have a look at the project page of A Grammar engine for Nguni natural language interfaces (GeNi), or come visit EKAW16 that will be held from 21-23 November in Bologna, Italy, where I will present the paper.

 

References

[1] Keet, C.M., Chirema, T. A model for verbalising relations with roles in multiple languages. 20th International Conference on Knowledge Engineering and Knowledge Management EKAW’16). Springer LNAI, 19-23 November 2016, Bologna, Italy. (in print)

[2] Leo, J. Modeling relations. Journal of Philosophical Logic, 2008, 37:353-385.

[3] Keet, C.M., Fillottrani, P.R. An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2. Data & Knowledge Engineering, 2015, 98:30-53.

[4] Calvanese, D., De Giacomo, G. The Description Logics Handbook: Theory, Implementation and Applications, chap. Expressive description logics, pp. 178-218. Cambridge University Press (2003).

[5] Keet, C.M., Khumalo, L. Toward a knowledge-to-text controlled natural language of isiZulu. Language Resources and Evaluation, 2016, in print.

[6] Keet, C.M., Khumalo, L. On the verbalization patterns of part-whole relations in isiZulu. Proceedings of the 9th International Natural Language Generation conference 2016 (INLG’16), Edinburgh, Scotland, Sept 2016. ACL, 174-183.

More stuff: relating stuffs and amounts of stuff to their parts and portions

With all the protests going on in South Africa, writing this post is going to be a moment of detachment of it (well, I’m trying), for it concerns foundational aspects of ontologies with respect to “stuff”. Stuff is the philosophers’ funny term for those kind of things that cannot be counted, or only counted in quantities, and are in natural language generally referred to by mass nouns. For instance, water, gold, mayonnaise, oil, and wine as kinds of things, yet one can talk of individual objects of them only in quantities, like a glass of wine, a spoonful of mayonnaise, and a litre of oil. It is one thing to be able to say which types of stuff there are [1], it is another matter how they relate to each other. The latter is described in the paper recently accepted at the 20th International Conference on Knowledge Engineering and Knowledge management (EKAW’16), entitled “Relating some stuff to other stuff” [2].

Is something like that even relevant, when students are protesting for free education, among other demands? Yes. At the end of the day, it is part and parcel of a healthy environment to live in. For instance, one should be able to realise traceability in food and medicine supply chains, to foster practices, and check compliance, of good production processes and supply chains, so that you will not buy food that makes you ill or take medicines that are fake [3,4]. Such production processes and product logistics deal with ‘stuffs’ and their portions and parts that get separated and put together to make the final product. Current implementations have only underspecified ‘links’ (if at all) that doesn’t let one infer automatically what (or who) the culprit is. Existing theoretical accounts from philosophy and in domain ontologies are incomplete, so they wouldn’t help you further either. The research described in the paper solves this issue.

Seven relations for portions and stuff-parts were identified, which have a temporal dimension where needed. For instance, the upper-half of the wine in your wine glass is a portion of the whole amount of wine in the glass, yet that amount of wine was a portion of the amount of wine in the bottle when you opened it, and yet it has as part some amount of alcohol. (Some reader may not find this example nice, for it being with alcohol, but Western Cape, where Cape Town is situated, is the wine region of the country.) The relations are structured in a little hierarchy, as informally depicted in the figure below.

Section of the basic taxonomy of part-whole relations of [5] (less and irrelevant sections in grey or suppressed), extended with the stuff relations and their position in the hierarchy.

Section of the basic taxonomy of part-whole relations of [5] (less and irrelevant sections in grey or suppressed), extended with the stuff relations and their position in the hierarchy.

Their formal definitions are included in the paper.

Another aspect of the solution is that it distinguishes between 1) the extensional and intensional level—like, between ‘an amount of wine’ and ‘wine’—because different constraints apply (following from that latter can be instantiated the former cannot), and 2) the amount of stuff and the (repeatable) quantity, as one can have 1kg of many things.

Just theory isn’t good enough, though, for one would want to use it in some way to indeed get those benefits of traceability in the supply chains. After considering the implementation options (see paper for details), I settled for an extension to the Stuff Ontology core ontology that now also imports a special purpose module OMmini of the Ontology of Units of Measure (see also the Stuff Ontology page). The latter sounds easier than that it worked in praxis, but that’s a topic of a different post. The module is there, and the links between the OMmin.owl and stuff.owl have been declared.

Although the implementation is atemporal in the end, it is still possible to do some automated reasoning for traceability. This is mainly thought availing of property chains to approximate the relevant temporal aspects. For instance, with scatteredPortionOf \circ portionOf \sqsubseteq scatteredPortionOf then one can infer that a scattered portion in my glass of wine that was a portion of bottle #1234 of organic Pinotage wine of an amount of wine, contained in cask #3, with wine from wine farm X of Stellar Winery from the 2015 harvest is a scattered portion of that amount of matter (that cask). Or take the (high-level) pharmaceutical supply chain from [4]: a portion (that is on a ‘pallet’) of the quantity of medicine produced by the manufacturer goes to the warehouse, of which a portion (in a ‘case’) goes to the distribution centre. From there, a portion ends up on the dispensing shelf, and someone buys it. Then tracing any customer’s portion of medicine—i.e., regardless the actual instance—can be inferred with the following chain: scatteredPortionOf \circ scatteredPortionOf \circ scatteredPortionOf \sqsubseteq scatteredPortionOf

Sure, the research presented hasn’t solved everything yet, but at least software developers now have a (better) way to automate traceability in supply chains. It also allows one to be more fine-grained in the analysis where a culprit may be, so that there are fewer cases of needless scares. For instance, we know that when there’s an outbreak of Salmonella, then we only have to trace where the batch of egg yolk went (typically in the tiramisu served in homes for the elderly), where it came from (which farm), and got mixed with in the production process, while the amounts of egg white on your lemon merengue still would be safe to eat even when it came from the same batch that had at least one infected egg.

I’ll be presenting the paper at EKAW’16 in November in Bologna, Italy, and hope to see you there! It’s not a good time of the year w.r.t. weather, but that’s counterbalanced by the beauty of the buildings and art works, and the actual venue room is in one of the historical buildings of the oldest university of Europe.

 

References

[1] Keet, C.M. A core ontology of macroscopic stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI vol. 8876, 209-224.

[2] Keet, C.M. Relating some stuff to other stuff. 20th International Conference on Knowledge Engineering and Knowledge Management EKAW’16). Springer LNAI, 19-23 November 2016, Bologna, Italy. (accepted)

[3] Donnelly, K.A.M. A short communication – meta data and semantics the industry interface: what does the food industry think are necessary elements for exchange? In: Proc. of Metadata and Semantics Research (MTSR’10). Springer CCIS vol. 108, 131-136.

[4] Solanki, M., Brewster, C. OntoPedigree: Modelling pedigrees for traceability in supply chains. Semantic Web Journal, 2016, 7(5), 483-491.

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

Automatically finding the feasible object property

Late last month I wrote about the updated taxonomy of part-whole relations and claimed it wasn’t such a big deal during the modeling process to have that many relations to choose from. Here I’ll back up that claim. Primarily, it is thanks to the ‘Foundational Ontology and Reasoner enhanced axiomatiZAtion’ (FORZA) approach which includes the Guided ENtity reuse and class Expression geneRATOR (GENERATOR) method that was implemented in the OntoPartS-2 tool [1]. The general idea of the GENERATOR method is depicted in the figure below, which outlines two scenarios: one in which the experts perform the authoring of their domain ontology with the help of a foundational ontology, and the other one without a foundational ontology.

generator

I think the pictures are clearer than the following text, but some prefer text, so here goes the explanation attempt. Let’s start with scenario A on the left-hand side of the figure: a modeller has a domain ontology and a foundational ontology and she wants to relate class two domain classes (indicated with C and D) and thus needs to select some object property. The first step is, indeed, selecting C and D (e.g., Human and Heart in an anatomy ontology); this is step (1) in the Figure.

Then (step 2) there are those long red arrows, which indicate that somehow there has to be a way to deal with the alignment of Human and of Heart to the relevant categories in the foundational ontology. This ‘somehow’ can be either of the following three options: (i) the domain ontology was already aligned to the foundational ontology, so that step (2) is executed automatically in the background and the modeler need not to worry, (ii) she manually carries out the alignment (assuming she knows the foundational ontology well enough), or, more likely, (iii) she chooses to be guided by a decision diagram that is specific to the selected foundational ontology. In case of option (ii) or (iii), she can choose to save it permanently or just use it for the duration of the application of the method. Step (3) is an automated process that moves up in the taxonomy to find the possible object properties. Here is where an automated reasoner comes into the equation, which can step-wise retrieve the parent class, en passant relying on taxonomic classification that offers the most up-to-date class hierarchy (i.e., including implicit subsumptions) and therewith avoiding spurious candidates. From a modeller’s viewpoint, one thus only has to select which classes to relate, and, optionally, align the ontology, so that the software will do the rest, as each time it finds a domain and range axiom of a relationship in which the parents of C and D participate, it is marked as a candidate property to be used in the class expression. Finally, the candidate object properties are returned to the user (step 4).

While the figure shows only one foundational ontology, one equally well can use a separate relation ontology, like PW or PWMT, which is just an implementation variant of scenario A: the relation ontology is also traversed upwards and on each iteration, the base ontology class is matched against relational ontology to find relations where the (parent of the) class is defined in a domain and range axiom, also until the top is reached before returning candidate relations.

The second scenario with a domain ontology only is a simplified version of option A, where the alignment step is omitted. In Figure-B above, GENERATOR would return object properties W and R as options to choose from, which, when used, would not generate an inconsistency (in this part of the ontology, at least). Without this guidance, a modeler could, erroneously, select, say, object property S, which, if the branches are disjoint, would result in an inconsistency, and if not declared disjoint, move class C from the left-hand branch to the one in the middle, which may be an undesirable deduction.

For the Heart and Human example, these entities are, in DOLCE terminology, physical objects, so that it will return structural parthood or plain parthood, if the PW ontology is used as well. If, on the other hand, say, Vase and Clay would have been the classes selected from the domain ontology, then a constitution relation would be proposed (be this with DOLCE, PW, or, say, GFO), for Vase is a physical object and Clay an amount of matter. Or with Limpopo and South Africa, a tangential proper parthood would be proposed, because they are both geographic entities.

The approach without the reasoner and without the foundational ontology decision diagram was tested with users, and showed that such a tool (OntoPartS) made the ontology authoring more efficient and accurate [2], and that aligning to DOLCE was the main hurdle for not seeing even more impressive differences. This is addressed with OntoPartS-2, so it ought to work better. What still remains to be done, admittedly, is that larger usability study with the updated version OntoPartS-2. In the meantime: if you use it, please let us know your opinion.

 

References

[1] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. Oct. 27 – Nov. 1, 2013, San Francisco, USA.

[2] Keet, C.M., Fernandez-Reyes, F.C., Morales-Gonzalez, A. Representing mereotopological relations in OWL ontologies with OntoPartS. 9th Extended Semantic Web Conference (ESWC’12), Simperl et al. (eds.), 27-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

New OWL files for the (extended) taxonomy of part-whole relations

Once upon a time (surely >6 years ago) I made an OWL file of the taxonomy of part-whole relations [1], which contains several parthood relations and a few meronyic-only ones that in natural language are considered ‘part’ but are not so according to mereology (like participation, membership). Some of these relations were defined with a specific domain and range that was a DOLCE category (it could just as well have been, say, GFO). Looking at it recently, I noticed it was actually a bit scruffy (but I’ll leave it here nonetheless), and more has happened in this area over the years. So, it was time for an update on contents and on design.

For the record on how it’s done and to serve, perhaps, as a comparison exercise on modeling, here’s what I did. First of all, I started over, so as to properly type the relations to DOLCE categories, with the DOLCE IRIs rather than duplicated as DOLCE-category-with-my-IRI. As DOLCE is way too big and slows down reasoning, I made a module of DOLCE, called DOLCEmini, mainly by removing the irrelevant object properties, though re-adding the SOB, APO and NAPO that’s in D18 but not in DOLCE-lite from DLP3791. This reduced the file from DOLCE-lite’s 534 axioms, 37 classes, 70 OPs, in SHI to DOLCEmini’s 388 axioms, 40 classes, 43 OPs, also in SHI, and I changed the ontology IRI to where DOLCEmini will be put online.

Then I created a new ontology, PW.owl, imported DOLCEmini, added the taxonomy of part-whole relations from [1] right under owl:topObjectProperty, with domain and range axioms using the DOLCE categories as in the definitions, under part-whole. This was then extended with the respective inverses under whole-part, all the relevant proper part versions of them (with inverses), transitivity added for all (as the reasoner isn’t doing it [2]) annotations added, and then aligned to some DOLCE properties with equivalences. This makes it to 524 axioms and 79 object properties.

I deprecated subquantityOf (annotated with ‘deprecated’ and subsumed by a new property ‘deprecated’). Several new stuff relations and their inverses were added (such as portions), and annotated them. This made it to the PW ontology of 574 axioms (356 logical axioms) and 92 object properties (effectively, for part-whole relations: 92 – 40 from dolce – 3 for deprecated = 49).

As we made an extension with mereotopology [3] (and also that file wasn’t great, though did the job nevertheless [4]), but one that not everybody may want to put up with, yet a new file was created, PWMT. PWMT imports PW (and thus also DOLCEmini) and was extended with the main mereotopological relations from [3], and relevant annotations were added. I skipped property disjointness axioms, because they don’t go well with transitivity, which I assumed to be more important. This makes PWMT into one of 605 (380 logical) axioms and 103 object properties, with, effectively, for parts: 103 – 40 from dolce – 3 for deprecated – 1 connection = 59 object properties.

That’s a lot of part-whole relations, but fear not. The ‘Foundational Ontology and Reasoner enhanced axiomatiZAtion’ (FORZA) and its tool that incorporates with the Guided ENtity reuse and class Expression geneRATOR (GENERATOR) method [4] describes a usable approach how that can work out well and has a tool for the earlier version of the owl file. FORZA uses an optional decision diagram for the DOLCE categories as well as the automated reasoner so that it can select and propose to you those relations that, if used in an axiom, is guaranteed not to lead to an inconsistency that would be due to the object property hierarchy or its domain and range axioms. (I’ll write more about it in the next post.)

Ah well, even if the OWL files are not used, it was still a useful exercise in design, and at least I’ll have a sample case for next year’s ontology engineering course on ‘before’ and ‘after’ about questionable implementation and (relatively) good implementation without the need to resorting to criticizing other owl files… (hey, even the good and widely used ontologies have a bunch of pitfalls, whose amount is not statistically significantly different from ontologies made by novices [5]).

 

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] 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 7603, 252-266.

[3] Keet, C.M., Fernandez-Reyes, F.C., Morales-Gonzalez, A. Representing mereotopological relations in OWL ontologies with OntoPartS. 9th Extended Semantic Web Conference (ESWC’12), Simperl et al. (eds.), 27-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

[4] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. Oct. 27 – Nov. 1, 2013, San Francisco, USA.

[5] Keet, C.M., Suarez-Figueroa, M.C., Poveda-Villalon, M. Pitfalls in Ontologies and TIPS to Prevent Them. In: Knowledge Discovery, Knowledge Engineering and Knowledge Management: IC3K 2013 Selected papers. Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (Eds.). Springer, CCIS 454, pp. 115-131. 2015.

An exhaustive OWL species classifier

Students enrolled in my ontology engineering course have to do a “mini-project” on a particular topic, chosen from a list of topics, such as on ontology quality, verbalisations, or language features, and may be theoretical or software development-oriented. In terms of papers, the most impressive result was OntoPartS that resulted in an ESWC2012 paper with the two postgraduate students [1], but also quite some other useful results have come out of it over the past 7 years that I’m teaching it in one form or another. This year’s top project in terms of understanding the theory, creativity to do something with it that hasn’t been done before, and working software using Semantic Web technologies was the “OWL Classifier” by Aashiq Parker, Brian Mc George, and Muhummad Patel.

The OWL classifier classifies an OWL ontology in any of its ‘species’, which can be any of the 8 specified in the standard, i.e., the 3 OWL 1 ones and the 5 OWL 2 ones. It also gives information on the DL ‘alphabet soup’—which axioms use which language feature with which letter, and an explanation of the letters—and reports on which axioms are the ones that violate a particular species. An example is shown in the following screenshot, with an exercise ontology on phone points:

phonePoints

The students’ motivation to develop it was because they had to learn about DLs and the OWL species, but Protégé 4.x and 5.x don’t tell you the species and the interfaces have only a basic, generic, explanation for the DL expressivity. I concur. And is has gotten worse with Protégé 5.0: if an ontology is outside OWL 2 DL, it still says the ‘old’ DL expressivity plus an easy-to-overlook tiny red triangle in the top-right corner once the reasoner was invoked (using Hermit 1.3.8) or a cryptic “internal reasoner error” message (Pellet), whereas with Protégé 4.x you at least got a pop-up box complaining about the ‘non-simple role…’ issues. Compare that with the neat feedback like this:

t15and16

It is also very ‘sensitive’—more so than one would be with Protégé alone. Any remote ontology imports have to be available at the location specified with the IRI. Violations due to wrong datatype usage is a known issue with the OWL Reasoner Evaluation set of ontologies, and which we’ve bumped into with the TDD testing as well. The tool doesn’t accept the invalid ones (wrong datatypes—one can select any XML data type in Protégé, but the OWL standard doesn’t support them all). In addition, a language such as OWL 2 QL has further restrictions on types of datatypes. (It is also not trivial to figure out manually whether some ontology is suitable for OBDA or not.) So I tried one from the Ontop website’s examples, presumably in OWL 2 QL:

fishdelish

Strictly speaking, it isn’t in OWL 2 QL! The OWL 2 QL profile does have xsd:integer as datatype [2], not xsd:int, as, and I quote the standard, “the intersection of the value spaces of any set of these datatypes [including xsd:integer but not xsd:int, mk] is either empty or infinite, which is necessary to obtain the desired computational properties”. [UPDATE 24-6, thanks to Martin Rezk:] The main toolset for OWL 2 QL, Ontop, actually does support xsd:int and a few other datatypes beyond the standard (e.g.: also float and boolean). There is similar syntax fun to be had with the pizza ontology: the original one is indeed in OWL DL, but if you open the file in Protégé 5 and save it, it is not in OWL DL anymore but in OWL 2 DL, for the save operation snuck in an owl#NamedIndividual. Click on the thumbnails below to see the before-and-after in the OWL classifier. This is not an increase in expressiveness—both are in SHOIN—just syntax and tooling.

pizzaOldpizzaP5

 

 

 

 

 

The OWL Classifier can thus classify both OWL 1 and OWL 2 ontologies, which it does through a careful orchestration of two OWL APIs: v1.4.3 was the last one to support OWL 1 species checking, whereas for the OWL 2 ontologies, the latest version is used (v4.2.3). The jar file and the source code are freely available on github for anyone to use and to take further. Turning it into a Protégé plugin very likely will make at least next year’s ontology engineering students happy. Comments, questions, and suggestion are welcome!

 

References

[1] Keet, C.M., Fernandez-Reyes, F.C., Morales-Gonzalez, A. Representing mereotopological relations in OWL ontologies with OntoPartS. 9th Extended Semantic Web Conference (ESWC’12), Simperl et al. (eds.), 27-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

[2] Boris Motik, Bernardo Cuenca Grau, Ian Horrocks, Zhe Wu, Achille Fokoue, Carsten Lutz, eds. OWL 2 Web Ontology Language: Profiles. W3C Recommendation, 11 December 2012 (2nd ed.).

Reflections on ESWC 2016: where are the ontologies papers?

Although I did make notes of the presentations I attended at the 13th Extended Semantic Web Conference a fortnight ago, with the best intentions to write a conference report, it’s going to be an opinion piece of some sort, on ontology engineering, or, more precisely: the lack thereof at ESWC2016.

That there isn’t much on ontology research at ISWC over the past several years, I already knew, both from looking at the accepted papers and the grapevine, but ESWC was still known to be welcoming to ontology engineering. ESWC 2016, however, had only one “vocabularies, schemas, and ontologies” [yes, in that order] session (and one on reasoning), with only the paper by Agnieszka and me solidly in the ‘ontologies’/ontology engineering bracket, with new theory, a tool implementing it, experiments, and a methodology sketch [1]. The other two papers were more on using ontologies, in annotating documents and in question answering. My initial thought was: “ah, hm, bummer, so ESWC also shifted focus”. There also were few ontologists at the conference, so I wondered whether the others moved on to a non-LD related field, alike I did shift focus a bit thanks/due to funded projects in adjacent fields (I did try to get funds for ontology engineering projects, though).

To my surprise, however, it appeared that a whopping 27 papers had been submitted to the “vocabularies, schemas, and ontologies” track. It was just that only three had made it through the review process. Asking around a bit, the comments were sort of like when I was co-chair of the track for ESWC 2014: ‘meh’, not research (e.g., just developing a domain ontology), minor delta, need/relevance unclear. And looking again at my reviews for 2015 and 2016, in addition to those reasons: failing to consider relevant related work, or a lacking a comparison with related work (needed to demonstrate improvement), and/or some issues with the theory (formal stuff). So, are we to blame and ‘suicidal’ or become complacent and lazy? It’s not like the main problems have been solved and developing an ontology has become a piece of cake now, compared to, say, 10 years ago. And while it is somewhat tempting to do some paper/presentation bashing, I won’t go into specifics, other than that at two presentations I attended, where they did show a section of an ontology, there was even the novice error of confusing classes with instances.

Anyway, there used to be more ontology papers in earlier ESWCs. To check that subjective impression, I did a quick-and-dirty check of the previous 12 editions as well, of which 11 had named sessions. Here’s the overview of the number of ontology papers over the years (minus the first one as it did not have named sections):

ontoPap

The aggregates are a bit ‘dirty’ as the 2010 increase grouped ontologies together with reasoning (if done for 2016, we’d have made it to 6), as was 2007 a bit flexible on that, and 2015 had 3 ontologies papers + 3 ontology matching & summarization, so stretching it a bit in that direction, as was the case in 2013. The number of papers in 2006 is indeed that much, with sessions on ontology engineering (3 papers), ontology evaluation (3), ontology alignment (5), ontology evolution (3), and ontology learning (3). So, there is indeed a somewhat downward trend.

Admitted, ‘ontologies’ is over the initial hype and it probably now requires more preparation and work to come up with something sufficiently new than it was 10 years ago. Looking at the proceedings of 5 years ago rather, the 7 ontologies papers were definitely not trivial, and I still remember the one on removing redundancies [2], the introduction of two new matching evaluation measures and comparison with other methods [3], and automatically detecting related ontology versions [4]. Five ontology papers then had new theory and some experiments, and two had extensive experiments [5,6]. 2012 had 6 ontologies papers, some interesting, but something like the ‘SKOS survey’ is a dated thing (nice, but ESWC-level?) and ISOcat isn’t great (but I’m biased here, as I don’t like it that noun classes aren’t in there, and it is hard to access).

Now what? Work more/harder on ontology engineering if you don’t want to have it vanish from ESWC. That’s easier said than done, though. But I suppose it’s fair to say to not discard the ESWC venue as being ‘not an ontology venue anymore’, and instead use these six months to the deadline to work hard enough. Yet, who knows, maybe we are harder to ourselves when reviewing papers compared to other tracks. Either way, it is something to reflect upon, as an 11% acceptance rate for a track, like this year, isn’t great. ESWC16 in general had good papers and interesting discussions. While the parties don’t seem to be as big as they used to be, there sure is a good time to be had as well.

 

p.s.: Cretan village, where I stayed for the first time, was good and had a nice short walk on the beach to the conference hotel, but beware that the mosquitos absent from Knossos Hotel all flock to that place.

 

References

[1] Keet, C.M., Lawrynowicz, A. Test-Driven Development of Ontologies. In: Proceedings of the 13th Extended Semantic Web Conference (ESWC’16). Springer LNCS 9678, 642-657. 29 May – 2 June, 2016, Crete, Greece.

[2] 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.

[3] Xing Niu, Haofen Wang, GangWu, Guilin Qi, and Yong Yu. Evaluating the Stability and Credibility of Ontology Matching Methods. In: Proceedings of the 8th Extended Semantic Web Conference (ESWC’11). Heraklion, Crete, Greece, 29 May – 2 June 2011. Springer LNCS 6643, 275-289.

[4] Carlo Alocca. Automatic Identification of Ontology Versions Using Machine Learning Techniques. In: Proceedings of the 8th Extended Semantic Web Conference (ESWC’11). Heraklion, Crete, Greece, 29 May – 2 June 2011. Springer LNCS 6643, 275-289.

[5] 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.

[6] Wei Hu, Jianfeng Chen, Hang Zhang, and Yuzhong Qu. How Matchable Are Four Thousand Ontologies on the Semantic Web. In: Proceedings of the 8th Extended Semantic Web Conference (ESWC’11). Heraklion, Crete, Greece, 29 May – 2 June 2011. Springer LNCS 6643, 290-304.