With apologies to Jeanette Winterson.
My fantasy funding would be to be given a large wad of cash to go and build an ontology for the sake of building an ontology; just as a record of what we know about a domain – modelling for fun. This might display a belief of an ontology being an “end in itself”; the truth is that I just like building ontologies. I do, however, believe strongly that this “ontology as an end in itself” is a bad thing and should be left as a hobby.
The authoring of an ontology is not an end in itself; it is a means to an end. In biology the “end” is to help us manage and analyse biology’s data with greater ease, reliability, replicability and so on. To this end the ontology is the servant and not the master; too much “truth and beauty” instead of “making things work”, whilst it can be fun, can result in debating upon how many angels can dance on the head of a pin – that is, largely fruitless explorations of how to resolve issues that appear so marginal that I could already be using something that did the job (but was full of ontological compromises ) while still waiting for the perfect solution.
In a similar vein, for the ontology evangelist, there is an ontological hammer for every representational nail. There’s so much more to knowledge representation than what Alan Rector and I have called “universal knowledge” in an Ontogenesis blog. This universal knowledge is the static things that are true for all instances of a class. There’s so much more that one can say about stuff though: things true for some instances of a class; stuff is tue contingent on some other knowledge; rules; higher-order statements, probabilistic knowledge and so on. All this stuff is not really “ontological”, but needs to be in semantic applications and is part of KR. Alan rector expanded on all of this in his keynote talk at the 2012 International Conference of Biomedical Ontology. (It is useful to note that one of Alan’s “definitions” of an ontology was along the lines of “whatever an ontology is, it isn’t that anything written in OWL” is an ontology- and this is certainly true and plays to this notion that there’s more to knowledge representation than ontology and OWL.
As a side-note, if we restrict the use of “ontology” to that which is about statements that are universally true about entities in a domain, then we change the acceptance criteria for what is an ontology. This then gets us out of the hole where all too often we’ll accept anything called an ontology as an ontology, then apply very strict evaluation criteria for what is a good ontology. If we can get to the state where MeSH is no longer criticised for being a bad ontology we’ll have got somewhere. MeSH is not an ontology; it’s a thesaurus – criticise it for being a bad thesaurus if it is a bad one, but don’t criticise it for being a bad ontology. This plea for a narrow interpretation of “ontology” is, however, not the same as saying all ontologies (of universal knowledge) should follow a particular ontological dogma.
Layered on top of these many kinds of statements in Kr world are views of knowledge for certain communities and purposes. Simon Jupp did a paper on view management in ontology. Here the aim was to separate out the ontological component of an OWL document from that which is used just to create effects in the application; typically this might be classes inserted to “gather” terms together for presentation purposes; hiding detail or abstraction that detracts from the users’ goals; use of relationships to provide appropriate navigation for application users. Both are necessary components of a KR system, but they shouldn’t all live in the OWL, ontological world. Simon separated them out into a SKOS layer for the view and navigation stuff, leaving the ontology component a bit cleaner. Other view mechanisms usually leave everything in the ontology….
So, rather than just the ontological hammer, we need a knowledge representation toolkit, of which the OntoHammer is just one piece (we also need the KR saw, plane, gimlet and sprocket wrench). Keeping our ontologies clean descriptions of our static knowledge of biology and using that as a framework upon which to:
- Generate views for different communities and usage profiles;
- hang rules and probablistic knowledge
- Combined with annotated data (and other types of knowledge) to form knowledgebases
- All sorts of other things…
Pragmatism and bredth of view are the order of the day. Ontology is necessary for KR but not sufficient.