Adding a little reality to the building of biological Ontologies

Abstract

Background: Many areas of biology are open to mathematical and computational modelling. The application of discrete, logical formalisms defines the field of biomedical ontologies. Ontologies have been put to many uses in bioinformatics. The most widespread is for description of entities about which data have been collected, allowing integration and analysis across multiple resources. There are now over 60 ontologies in active use, increasingly developed as large, international collaborations.

There are, however, many opinions on how ontologies should be authored; that is, what is appropriate for representation. Recently, a common opinion has been the “realist” approach that places restrictions upon the style of modelling considered to be appropriate.

Methodology/Principle Findings: Here, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models.

Conclusions/Significance: From our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology. Introduction

Authors

Phillip Lord, Newcastle University Robert Stevens, University of Manchester

Introduction

Ontologies are now widely used for describing and enhancing biological resources and biological data, largely following on from the success of the Gene Ontology ¹ . Ontologies have been used for many purposes, from schema integration to value reconcilliation to query interfaces [2]. Ontologies have also become a cornerstone of computational biology and bioinformatics. As computationally amenable artifacts they are, themselves, a direct part of computational biology; many computational biologists are involved in their production and maintenance. Many more use ontologies to summarise their data, often by looking for over-representation ² , as the basis for drawing computational inferences about data ³ , or as the basis for determining semantic similarity ⁴ . Even those not making direct computational use of ontologies are likely to come into contact with them, for example, when preparing annotation as part of their data release ⁵ .

It is, therefore, of vital interest to computational biologists that ontologies for use within biomedicine are fit for purpose. One effort that aims to increase the quality of the ontologies available within biomedicine is the “OBO Foundry” ⁶ . The main tool that it uses for this is “an evolving set of shared principles governing ontology development”. The initial eleven principles of the OBO Foundry [8] were largely concerned with what might be termed ‘good engineering practice’ (ontologies must, for example, be openly available, with a common syntax, well documented, and used). These principles have later been joined by a further eleven [9]; these include principles such as “textual definitions will use the genus-species form”, “Use of Basic Formal Ontology” and, the somewhat quixotic, “terms […] should correspond to instances in reality”. These stem not from engineering practice, but from a perspective called realism.

The many different uses for ontologies that we have described are reflected in different understandings and methodologies about how and what to represent in an ontology. Over the last few years, for many uses the paradigm has moved from “a conceptualization of the application domain” toward “a description of the key entities in reality”; it is this latter approach that defines realism ⁷ . This approach to ontology is typified by the Basic Formal Ontology (BFO); a small upper-ontology for use within science in general and biomedical ontology building in particular ⁸ .

There has been significant discussion regarding the possibility of representing only “real entities” in computational ontologies [12]. Likewise, there has been significant discussion about the philosophy surrounding realism and the role of ontology in its representation ⁷ . While it is argued by some that it is possible to represent only reality when making a domain description, there has, however, been little discussion on whether it is necessarily desirable to do so.

In this paper, we consider the implications that realism has for the choices that are open to the ontologist while they are modelling their domain of interest. In particular, we consider the implications that this has for the computational capabilities of any resultant ontology, in terms of its ability to represent scientific knowledge in a computationally amenable form, as well as the ability to perform automated inference or statistics over this knowledge. We suggest that the application of realism results in ontologies that are over-complex, awkward or limited; as such, realism falls far short of its aim of increasing the fitness-for-purpose of ontologies. This approach, therefore, is unlikely to fulfil the needs of computational biologists whom form a substantial part of both the user and developer community for bio-ontologies.

Bibliography