A strike against the ‘realism-based approach’ to ontology development

The ontology engineering course starting this Monday at the Knowledge Representation and Reasoning group at Meraka commences with the question What is an ontology? In addition to assessing definitions, it touches upon long-standing disagreements concerning if ontologies are about representing reality, our conceptualization of entities in reality, or some conceptualization that does not necessarily ascribe to existence of reality. The “representation of reality school” is advocated in ontology engineering most prominently by Barry Smith and cs. and their foundational ontology BFO, the “conceptualization of entities in reality school” by various people and research groups, such as the LOA headed by Nicola Guarino and their DOLCE foundational ontology, whereas the “conceptualization irrespective regardless reality school” can be (but not necessarily is) encountered in organisations developing, e.g., medical ontologies that do not ascribe to evidence-based medicine to decide what goes in the ontology and how (but instead base it on, say, the outcome of power plays between big pharma and health insurance companies).

Due to the limited time and scope of this and previous courses on ontology engineering I taught, I mention[ed] only succinctly that those differences exist (e.g., pp10-11 of the UH slides), and briefly illustrate some of the aspects of the debate and their possible consequences in practical aspects of ontology engineering. This information is largely based on a few papers and extracting consequences from that, the examples they describe and that I encountered, and the discussions that took place at the various meetings, workshops, conferences, and summer schools that I participated in. But there was no nice, accessible, paper that describes de debate—or even part of it—more precisely and is readable also by ontologists who are not philosophers. Until last week, that is. The Applied Ontology journal published a paper by Gary Merrill, entitled Ontological realism: Methodology or misdirection? [1], that assess critically the ontological realism advocated by Barry Smith and his colleague Werner Ceusters. Considering its relevance in ontology engineering, the article has been made freely available, and in the announcement of the journal issue, its editors in chief (Nicola Guarino and Mark Musen) mentioned that Smith and Ceusters are busy preparing a response on Merrill’s paper, which will be published in a subsequent issue of Applied Ontology. Merrill, in turn, promised to respond to this rebuttal.

But for now, there are 30 pages of assessment on the merits of, and problems with, the philosophical underpinnings of the “realism-based approach” that is used in particular in the realm of ontology engineering within the OBO Foundry project and its large set of ontologies, BFO, and the Relation Ontology. The abstract gives an idea of the answer to the question in the paper’s title:

… The conclusion reached is that while Smith’s and Ceusters’ criticisms of prior practice in the treatment of ontologies and terminologies in medical informatics are often both perceptive and well founded, and while at least some of their own proposals demonstrate obvious merit and promise, none of this either follows from or requires the brand of realism that they propose.

The paper’s contents backs this up with analysis, arguments, examples, and bolder statements than the abstracts suggests.
For anyone involved in ontology development and interested in the debate—even if you think you’re tired of it—I recommend reading the paper, and to at least follow how the debate will unfold with responses and rebuttals.

My opinion? Well, I have one, of course, but this post is an addendum to the general course page of MOWS’10, hence I try to refrain from adding too much bias to the course material.

UPDATE (27-7-2010): On whales and apples, and on ontology and reality: you might enjoy also “Moby Dick: an exercise in ontology”, written by Lorne A. Smith.


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

New book on Novel Developments in Granular Computing

Late 2008 I mentioned the forthcoming invited book chapter [1] I wrote for “Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation”, edited by JingTao Yao. Finally, it has been published.

The topics of the book focus on modelling with/representation of granularity, rough sets and logic, data mining, classification, and fuzzy aspects; see the preface and abstracts of the 19 chapters. The free sample chapter is an interesting analysis by Yiyu Yao on Human-Inspired Granular Computing (see menu bar on the left of the page). My contribution is in the modelling section: basically, the book chapter is a self-contained version of chapter 2 of my PhD thesis, with some minor additions from chapters 4 and 5; in short:

Multiple different understandings and uses exist of what granularity is and how to implement it, where the former influences success of the latter with regards to storing granular data and using granularity for automated reasoning over the data or information, such as granular querying for information retrieval. We propose a taxonomy of types of granularity and discuss for each leaf type how the entities or instances relate within its granular level and between levels. Such distinctions give guidelines to a modeler to better distinguish between the types of granularity in the design phase and the software developer to improve on implementations of granularity. Moreover, these foundational semantics of granularity provide a basis from which to develop a comprehensive theory of granularity.

Anyone who has published with IGI before knows about the unusual editing policies and their preferred layout; hence, I will upload the latex-ed preprint soon… here is the preprint.


[1] Keet, C.M. A top-level categorization of types of granularity. In: Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation. JingTao Yao (Ed.). IGI Global. 2010. pp81-117.