Ontologies for better communication

I just returned from an interesting summer school [1] about reasoning on the Web with rules and ontologies and their possible applications for the life sciences. As per usual, discussions about the covered material and related topics continued during the breaks and dinners. One of those aspects was about the aim of ontologies – that is, the plural that generally refers to the engineering artifacts, and not the singular that focuses on philosophical considerations.

Notwithstanding the pictures in Silvie Spreeuwenberg and Michael Schroeder’s presentations that ontologies (aim to or are used to) facilitate machine-machine communication, Ben Good was convinced that the ultimate aim of ontologies is to improve human-to-human communication. For instance, the Gene Ontology project [2] started because geneticists investigating genes of different types of organisms wanted to link up the data in their databases. This can be seen as linking and integrating databases, i.e. machine-machine communication, but also that it is ultimately humans from different research communities who want to share a common vocabulary to communicate and do cross-species research. Likewise, ontologies for content negotiation and mediation among software agents can be seen in the light of human-to-human communication. Sometimes, if one stretches the context. For instance, if you book a ticket online, then your software interacts with the software of the ticket-seller and the whole point is to reduce, or even eliminate, user-user interaction. But of course, when you extend the context, you could argue that behind the software of the ticket-seller there is, indirectly, a human being – although one who does not want to interact with you personally. Idem ditto linking up other resources on the Web, like biological databases and workflows: you don’t want bother some curator personally, but access the data directly through machine-machine communication facilitated with an ontology to ensure your and their data is of the same type.In addition, it has been noted before (e.g. [3]) that ontologies are for run-time access to represented knowledge in order to improve computation for data- information- and knowledge management, i.e. models intended to be machine-interpretable but not necessarily human-readable.

Another factor in favour of the ontologies for human-to-human communication is to see an ontology as a (formal) representation of a scientific theory, advocated by e.g. Barry Smith and cs [4]. I have made a more cautious observation [5] that it appeared to be a nice ‘side effect’ of the ontologies-approach – in the scope of that research (semi-automated ontology development of eco-ontologies based on STELLA models). The disambiguated formal representation would make scientific discourse easier, according to the user. However, this approach tends toward the philosophical notion of Ontology as a representation of reality, not the engineering artifacts of computer science & IT that have to have an immediate use, competitive advantage, and return on investment through database integration, software agent mediation and the like (see [6] on Ontology-driven Information Systems for a useful introduction).

A third aim for ontologies can be to improve communication between human and computer. However, at present this more of an intermediate aim, because improving human-computer interaction to improve on the ontology development and maintenance aims to improve the quality of an ontology, which then is ultimately used for human-human or computer-computer communication. For instance, one can verbalize the formal ontologies and conceptual models [7], which results in a for the domain expert understandable rendering of the formalisms into fixed-syntax pseudo-natural language statements instead of, say, first order logic or description logic axioms. This option is since quite some time available for the English language and ORM models and supported in software like VisioModeler and Microsoft Visio, and now available for 10 languages in DogmaModeler [8].

Taking a consensus-approach, one could say that it depends on the (sub-)goal of deployment of ontologies, which are intended primarily to improve either human-human or machine-machine communication. From an engineering perspective, the latter is certainly more important, but if one develops domain ontologies and interacts with the domain experts, putting an emphasis on the former may be more effective to achieve its adoption among a broader user base.

References

[1] ReasoningWeb http://reasoningweb.org/2006/.
[2] Gene Ontology http://www.geneontology.org/.
[3] Jarrar, M., Demy, J., Meersman, R. On Using Conceptual Data Modeling for Ontology Engineering. Journal on Data Semantics Special issue on “Best papers from the ER/ODBASE/COOPIS 2002 Conferences”, 2003, 1(1): 185-207.
[4] OBO Foundry http://www.obofoundry.org/.
[5] Keet, C.M. Factors affecting ontology development in ecology. Data Integration in the Life Sciences 2005 (DILS2005), Ludaescher, B, Raschid, L. (eds.). San Diego, USA, 20-22 July 2005. Lecture Notes in Bioinformatics 3615, Springer Verlag, 2005. pp46-62.
[6] Guarino, N. Formal Ontology and Information Systems. Formal Ontology in Information Systems, Proceedings of FOIS’98, Trento, Italy, Amsterdam: IOS Press. 1998.
[7] Jarrar, M., Keet, C.M., Dongilli, P. Multilingual verbalization of ORM conceptual models and axiomatized ontologies. STARLab Technical Report, Vrije Universiteit Brussel. February 2006.
[8] Technical reports with an example for each supported language (English, Dutch, German, Italian, Spanish, Catalan, French, Arabic, Russian, and Lithuanian): http://www.starlab.vub.ac.be/staff/mustafa/orm/verbalization/.

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