Ontological realism, methodologies, and mud slinging: a few notes on the AO trilogy

In July at the start of the MOWS’10 course on ontology engineering I pointed to more background literature about the debate about ontology as reality representation, its principle references, the new comprehensive assessment on its problems by Gary Merrill [1], and I included the note from the Applied Ontology Journal editors that Barry Smith and Werner Ceusters were writing a comprehensive rebuttal, to which Merrill would response in turn. They’re out now [2,3], and also freely available through the dedicated AO page.

On cursory glance seeing some juicy sentences, Smith and Ceusters’ 50-page reply [2] seemed like a good pastime to read on the gray, rainy, and cold Sunday afternoon last week and to ponder if and how I would incorporate it in an updated version of the ontology engineering course. It, however, contains many harsh statements with the main message that they’re doing a great thing with their so-called “realist methodology” and that Merrill’s critique is irrelevant. Merrill’s 30-page response to that [3], which I finished reading recently, is that Smith and Ceusters’ clarification made matters worse and thereby confirming it is a misdirection.

So, what to make of all that? If I were a VIP in ontology engineering, I would ask the AO editors to write a proper reply to the Smith and Ceusters’ (BS & WC) paper. But I am not; hence, I will mention a few aspects on my blog only (which might me do more harm than good, but I hope not). I will start with a note on realism, then the usage of the term “application ontologies”, and finally claims about BS & WC’s “realist methodology” that is not a methodology.

Notes on realism

On the realism dimension of the debate, I have not much to say. I subscribe to the, what Merrill formulates as the, “Empiricist Doctrine” [1], which states that “the terms of science… are to be taken to refer to the actually existing entities in the real world”, especially when it comes to ontologies for the life sciences and (bio)medicine. If you want an ontology of deities, fairies, or other story characters, that’s fine, too—just do not put them in a bio-ontology. What I had understood from the conversations, presentations, and papers of BS & WC is that if you accept the “Empiricist Doctrine”, then so you must go along with universals (as opposed to concepts). Merrill calls the latter component the “Universalist Doctrine” where “the terms of science… are to be understood as referring directly to universals”, which is one of many metaphysical stances [1]. I do not know if I subscribe to universals and I do not care about that that much. Although I did some philosophy of science and philosophy of nature a while ago and read up on other subjects in philosophy in the past few years, I am not a philosopher by training and do not know about all intricacies of all alternatives around (but maybe I should).

Another reason for my misunderstanding—or: conflating the two doctrines—is also due to the fact that descriptions and definitions in the BS & WC papers are not consistent throughout (elaborated on by Merrill [1,3]). For instance, in [4], ontology is taken as reality representations, but in [2] it is reality representation that is described by science, i.e., as scientists understand it, or in other words: the representation of the theories. Thus, where the things in the ontology are terms that do not have a ‘direct link’ to the actual entities, but they go through the scientists’ mind with their conceptualizations of reality. This is quite a difference from [4]. Make of it what you like.

Last, the ‘funny’ thing is that when you use the Empiricists Doctrine it does not matter if you use BFO, DOLCE, GFO, or whichever foundational ontology for practical ontology development. The current formalisations of BFO, DOLCE or any of the others do not have in their formalisation that the categories [unary predicates] denote either universals or concepts. Clearly, the communication of the informal intentions would be different if the top (OWL:thing or similar) in the ontology is called Universal or Concept, but in BFO it is called Entity and in DOLCE it is Particular. Thus, de facto, neither one commits to one philosophical doctrine or another in the top-level categorization and formalisation.

What are “application ontologies”?

Smith and Ceusters in [4] make a distinction between reference ontologies and application ontologies, the former intended to represent “settled science” and latter that part of science that is in flux. This rather difficult to maintain distinction is discussed at length in [3]. What I wish to add, and which was only mentioned in passing in [3], is that the notion of ‘application ontologies’ elsewhere in the ontologies enterprise is used quite differently. It refers to OWL- or DL-formalised conceptual data models modeled in one of the common conceptual data modelling languages (UML, EER, ORM), but not real ontologies. The discussion about the difference between an ontology and a conceptual data model is beyond the current scope, but it is important to note that the same term means something different in pretty much all other literature about ontologies. Perhaps BS & WC have not read that literature, given that they happily attack computer science, knowledge engineering, and conceptual modelling (section 3.1 in [2]) with ‘justifications’ that Wüster-the-businessman over at ISO is a telling example of knowledge engineering and conceptual modelling (he is not), and that it was the training in cognitive psychology we all got as computer scientists (we did not) that makes us confused and stick to concepts instead of buying into universalist doctrine. Such statements are not helpful.

Either way, application ontology as a formal conceptual data model is definitely a more tenable definition [setting aside if one agrees with it] than application ontology for the non-settled science for the fact that there is no crisp boundary between settled and non-settled science. If the vague distinction is not enough already to complicate the debate: concepts are allowed to appear in BS & WC’s application ontologies.

About “methodologies”

Smith and Ceusters propose their “realist methodology” in section 1 of [2], but a methodology it is not—at least, not in the sense I, and (m)any other people in CS & IT, use the term. What BS & WC put forward is a set of principles. It does not say what to do, how, and when. And there is no empirical validation that the resultant ontologies are better (validation sensu a proper scientific experiment with subjects with/without using the ‘methodology’, measurable quality criteria, statistically significant, etc.).

An example of a fairly straightforward methodology for ontology development is METHONTOLOGY (among others [5]), and a more recent one for collaborative distributed ontology development: the NeON Methodology [6]. The latter has a nice fairly comprehensive overview picture of the interactions of the different steps (see Fig. 1, below) that are described further in [6] (and an aspect of this are the interactions between the different steps [7]). In my lectures, I like to be impartial and include a variety of options to sensitise  ontology developers to the plethora of options (see, e.g., Sections 3 and 4 of the MOWS’10 course, which is an updated version of SemWebTech lecture 3+4, where the what comes before the how, outlined in SemWebTech lecture 5: Methods and Methodologies), but a set of principles that is labeled “methodology” is not something that fits in a real methodology section (though they may well fit in another module).

How can BS & WC even dare to propose a methodology for ontology development when disregarding all literature on ontology development (except for the OntoClean method)? If their methodology is so superior, than give me evidence why and how it is better than all the methodologies that have been proposed over the past 15 years or so. Spoon-feed me about the shortcomings of those procedures; that is, not a lecture about the realist vs anti-realist, conceptualist and what have you, but why I should not buy into collecting non-ontological resources, looking at ontology design patterns, providing intermediate steps for the formalization, and so forth.

Whilst reading section 1 of [2], I have been trying to extract a methodology—that is, reading it with a positive attitude to try to make something of it—but could find little, and what I extracted from it, is not enough for practical ontology development and maintenance. As example, let us take the step of  “non-ontological resource reuse” for the chosen subject domain. In an ontology engineering methodology, this includes options, such as assessing chosen sources such as relevant thesauri, databases, natural language text, and methods for each option, i.e., the how-to to reuse the non-ontological resources, such as the manual database reverse engineering steps vs. semi-automated tools (in, say, VisioModeler, or the Protégé plugin Lubyte  developed [8]), data mining and clustering, the different methods to extract terms from text etc. From [2], e.g. section 1.13, I gather that the only way to execute this step of “non-ontological resource reuse” is that domain experts manually read the scientific literature and manually add the knowledge to the ontology. No help from, say, the KEGG, AGROVOC, ICD10, or ontologies that were already developed by other groups—all that should be ignored—let alone automating anything to find, say, candidate terms automatically with NLP tools. That surely must be a joke (or oversight, or sheer ignorance) and does not reflect what happens during the development of OBO ontologies. Or take, e.g., METHONTOLOGY or MoKi’s stage of intermediate representations between de domain expert’s informal representation and the formalisation of it in a suitable logic language, such as pseudo-natural language, diagrams as syntactic sugar for the underlying logic, the Protégé and OBOEdit ODEs: are they to be ignored, too? Of course not; well, I presume that that is not the intention of BS & WC’s “methodology”.

They may have enjoyed having written a trashing of 20 years of knowledge engineering and conceptual data modelling whose outputs apparently can be ignored, but there surely is room to learn a thing or two about it. After reading up on the related works on methodologies, they can make a real attempt at developing a methodology that satisfies the set of principles, be that by developing a methodology from scratch or integrating it into (or extending) existing methodologies. Until then, what is presented in section 1 of [2] will not—cannot—be added to a ‘methods and methodologies’ module in an ontology engineering course.

P.S.: Other views

A different online debate about realism in ontology engineering can be read over at Phil Lord’s blog (The Status quo farewell tour on realism, Why not?, and Why realism is wrong) and his paper together with Robert Stevens at PLoS ONE [9], versus David Sutherland’s Realism, Really? and Yes, really in favour of the realist approach for practical ontology development. Then there is the OBO-Foundry discussion list, and, e.g., a paper in FOIS’10 by Michel Dumontier and Robert Hoehndorf [10], and undoubtedly more papers about the issues raised in the AO trilogy will follow.

References

[1] Gary H. Merrill. Ontological realism: Methodology or misdirection? Applied Ontology, 5 (2010) 79–108.

[2] Barry Smith and Werner Ceusters. Ontological realism: A methodology for coordinated evolution of scientific ontologies. Applied Ontology, 5 (2010) 79–108.

[3] Gary H. Merrill. Realism and reference ontologies: Considerations, reflections and problems. Applied Ontology, 5 (2010) 79–108.

[4] Barry Smith. Beyond Concepts, or: Ontology as Reality Representation. Achille Varzi and Laure Vieu (eds.), Formal Ontology and Information Systems. Proceedings of the Third International Conference (FOIS 2004), Amsterdam: IOS Press, 2004, 73-84.

[5] Corcho, O., Fernandez-Lopez, M. and Gomez-Perez, A. (2003). Methodologies, tools and languages for building ontologies. Where is their meeting point?. Data & Knowledge Engineering 46(1): 41-64.

[6] Mari Carmen Suarez-Figueroa, Guadalupe Aguado de Cea, Carlos Buil, Klaas Dellschaft, Mariano Fernandez-Lopez, Andres Garcia, Asuncion Gomez-Perez, German Herrero, Elena Montiel-Ponsoda, Marta Sabou, Boris Villazon-Terrazas, and Zheng Yufei. NeOn Methodology for Building Contextualized Ontology Networks. NeOn Deliverable D5.4.1. 2008.

[7] Keet, C.M. Dependencies between Ontology Design Parameters. International Journal of Metadata, Semantics and Ontologies, 2010, 5(4): 265-284.

[8] Lina Lubyte. Techniques and Tools for the Design of Ontologies for Data Access. PhD Thesis, Free University of Bozen-Bolzano, KRDB Dissertation Series DS-2010-02, 2010.

[9] Lord, P. & Stevens, R. Adding a little reality to building ontologies for biology. PLoS One, 2010, 5(9), e12258. DOI: 10.1371/journal.pone.0012258.

[10] Dumontier, M. & Hoehndorf, R. Realism for scientific ontologies. In: Proceeding of the Conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010), 387–399. Amsterdam: IOS Press.

Fig 1. Graphical depiction of different steps in ontology development, where each step has its methods and interactions with other steps (taken from 6).

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72010 SemWebTech lecture 5: Methods and Methodologies

The previous two lectures have given you a basic idea about the two principal approaches for starting developing an ontology—top-down and bottom-up—but they do not constitute an encompassing methodology to develop ontologies. In fact, there is no proper, up-to-date comprehensive methodology for ontology development like there is for conceptual model development (e.g., [1]) or ‘waterfall’ versus ‘agile’ software development methodologies. There are many methods and, among others, the W3C’s Semantic Web best practices, though, which to a greater or lesser extent can form part of a comprehensive ontology development methodology.

As a first step towards methodologies that gives a general scope, we will look at a range of parameters that affect ontology development in one way or another [2]. There are four influential factors to enhance the efficiency and effectiveness of developing ontologies, which have to do with the purpose(s) of the ontology; what to reuse from existing ontologies and ontology-like artifacts and how to reuse them; the types of approaches for bottom-up ontology development from other legacy sources; and the interaction with the choice of representation language and reasoning services.

Second, methods that helps the ontologist in certain tasks of the ontology engineering process include, but are not limited to, assisting the modelling itself, how to integrate ontologies, and supporting software tools. We will take a closer look at OntoClean [3] that contributes to modelling taxonomies. One might ask oneself: who cares, after all we have the reasoner to classify our taxonomy anyway, right? Indeed, but that works only if you have declared many properties for the classes, which is not always the case, and the reasoner sorts out the logical issues, but not the ontological issues. OntoClean uses several notions from philosophy, such as rigidity, identity criteria, and unity [4, 5] to provide modelling guidelines. For instance, that anti-rigid properties cannot subsume rigid properties; e.g., if we have, say, both Student and Person in our ontology, the former is subsumed by the latter. The lecture will go into some detail of OntoClean.

If, on the other hand, you do have a rich ontology and not mostly a bare taxonomy, ‘debugging’ by availing of an automated reasoner is useful in particular with larger ontologies and ontologies represented in an expressive ontology language. Such ‘debugging’ goes under terms like glass box reasoning [6], justification [7], explanation [8], and pinpointing errors. While they are useful topics, we will spend comparatively little time on it, because it requires some more knowledge of Description Logics and its (mostly tableaux-based) reasoning algorithms that will be introduced only in the 2nd semester (mainly intended for the EMCL students). Those techniques use the automated reasoner to at least locate modelling errors and explain in the most succinct way why this is so, instead of just returning a bunch of inconsistent classes; proposing possible fixes is yet a step further (one such reasoning service will be presented in lecture 6 on Dec. 1).

Aside from parameters, methods, and tools, there are only few methodologies, which are even coarse-grained: they do not (yet) contain all the permutations at each step, i.e. what and how to do each step, given the recent developments. A comparatively comprehensive one is Methontology [10], which has been applied to various subject domains (e.g., chemicals, legal domain [9,11]) since its development in the late 1990s. While some practicalities are superseded with new [12] and even newer languages and tools, some of the core aspects still hold. The five main steps are: specification, conceptualization (with intermediate representations, such as in text or diagrams, like with ORM [1] and pursued by the modelling wiki MOKI that was developed during the APOSDLE project for work-integrated learning), formalization, implementation, and maintenance. Then there are various supporting tasks, such as documentation and version control.

Last, but not least, there are many tools around that help you with one method or another. WebODE aims to support Methontology, the NeOn toolkit aims to support distributed development of ontologies, RacerPlus for sophisticated querying, Protégé-PROMPT for ontology integration (there are many other plug-ins for Protégé), SWOOGLE to search across ontologies, OntoClean with Protégé, and so on and so forth. For much longer listings of tools, see the list of semantic web development tools, the plethora of ontology reasoners and editors, and range of semantic wiki projects engines and features for collaborative ontology development. Finding the right tool to solve the problem at hand (if it exists) is a skill of its own and it is a necessary one to find a feasible solution to the problem at hand. From a technologies viewpoint, the more you know about the goals, features, strengths, and weaknesses of available tools (and have the creativity to develop new ones, if needed), the higher the likelihood you bring a potential solution of a problem to successful completion.

References

[1] Halpin, T., Morgan, T.: Information modeling and relational databases. 2nd edn. Morgan Kaufmann (2008)

[2] Keet, C.M. Ontology design parameters for aligning agri-informatics with the Semantic Web. 3rd International Conference on Metadata and Semantics (MTSR’09) — Special Track on Agriculture, Food & Environment, Oct 1-2 2009 Milan, Italy. F. Sartori, M.A. Sicilia, and N. Manouselis (Eds.), Springer CCIS 46, 239-244.

[3] Guarino, N. and Welty, C. An Overview of OntoClean. in S. Staab, R. Studer (eds.), Handbook on Ontologies, Springer Verlag 2004, pp. 151-172

[4] Guarino, N., Welty, C.: A formal ontology of properties. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNAI, vol. 1937, pp. 97–112. Springer, Heidelberg (2000)

[5] Guarino, N., Welty, C.: Identity, unity, and individuality: towards a formal toolkit for ontological analysis. In: Proc. of ECAI 2000. IOS Press, Amsterdam (2000)

[6] Parsia, B., Sirin, E., Kalyanpur, A. Debugging OWL ontologies. World Wide Web Conference (WWW 2005). May 10-14, 2005, Chiba, Japan.

[7] M. Horridge, B. Parsia, and U. Sattler. Laconic and Precise Justifications in OWL. In Proc. of the 7th International Semantic Web Conference (ISWC 2008), Vol. 5318 of LNCS, Springer, 2008.

[8] Alexander Borgida, Diego Calvanese, and Mariano Rodriguez-Muro. Explanation in the DL-Lite family of description logics. In Proc. of the 7th Int. Conf. on Ontologies, DataBases, and Applications of Semantics (ODBASE 2008), LNCS vol 5332, 1440-1457. Springer, 2008.

[9] Fernandez, M.; Gomez-Perez, A. Pazos, A.; Pazos, J. Building a Chemical Ontology using METHONTOLOGY and the Ontology Design Environment. IEEE Expert: Special Issue on Uses of Ontologies, January/February 1999, 37-46.

[10] Gomez-Perez, A.; Fernandez-Lopez, M.; Corcho, O. Ontological Engineering. Springer Verlag London Ltd. 2004.

[11] Oscar Corcho, Mariano Fernández-López, Asunción Gómez-Pérez, Angel López-Cima. Building legal ontologies with METHONTOLOGY and WebODE. Law and the Semantic Web 2005. Springer LNAI 3369, 142-157.

[12] Corcho, O., Fernandez-Lopez, M. and Gomez-Perez, A. (2003). Methodologies, tools and languages for building ontologies. Where is their meeting point?. Data & Knowledge Engineering 46(1): 41-64.

Note: references 2, 3, and 9 are mandatory reading, 6, 7, and 10 recommended, and 1, 4, 5, 8, 11, and 12 are optional.

Lecture notes: lecture 5 – Methodologies

Course webpage