Thursday, December 26, 2013


I've launched into a project at last, a terminological database toolset that's been on the drawing board for a very long time indeed (with what I hope will prove to be an accompanying business model to harness it all), and one thing that I ran across in my initial data scheme for termbases is the "context" field. Logically, that context is an ontological specification - a kind of "where am I?" in the larger scheme of the vocabulary of the full language - and it's used to draw distinctions about the terminology used in specific applications.

Well, so I delved into the available literature about ontology tools. Of which there are many.  And I hadn't really looked in many years; they've proliferated, especially in the context of the semantic web and bioinformatics, so here's a partial linkdump of some of the information that looks most promising.

  • A decent overview.
  • KIF = Knowledge Interchange Format [here] [in SUMO]. This is a declarative language with LISP syntax used to express first-order logic predicates about concepts.
  • SUMO = Suggested Upper Merged Ontology. Sort of the basic list of concepts that underlie everything else.
  • Tips on ontology development. And pitfalls.
  • A few basic tutorials about the semantic web. It's based on a graph database model (for semantic networks).
  • RDF is used to encode chunks of graph data in the semantic web (it can also be embedded in HTML, of course).
  • Ontological data about RDF documents is encoded in RDFS and OWL.
  • OntoSelect is apparently a cataloging service for ontologies found/discovered on the Semantic Web - here's a mention, but the service itself seems to be down.
  • Biology is another area where ontologies are used extensively; here, for example, is the Experimental Factor Ontology. Note that it is downloadable in OWL format. Experimental ontologies are generally available as free, open-source data, while anything with any hint of commercial usefulness is blisteringly expensive (pharmacovigilance, for example - the adverse effects ontology used for drug side effect reporting).
  • The Gene Expression Atlas is also ontology-based. This is a real-world application of something that used to be considered hard AI, and I find that pretty fascinating in and of itself.
  • Aaand a protein ontology that I've linked partly because proteins are inherently cool and partly because the legend is so pretty.
  • Bioinformatics ontologies aren't always published in OWL; OBO is a competing standard. The Obofoundry catalogs a bunch of ontologies.
  • is an ontology viewer for ontologies published on the Web. Here's the display for an adverse event ontology.
There are reams and reams of information about ontologies these days. Those are the more interesting things I ran across while determining that I don't need to go into that kind of depth to do what I need to do.

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