2.0 Ontology

Hi There,

I'm sorry if I'm being a bit dense but what does this mean?...

"2010-02-25 There has been a decision in the release of DMOZ 2.0 that we would go back and use the DMOZ 1.0 ontology. This means that we will no longer deliver the 2.0 ontology folder and there will be the single delivery. Always watch this file should the editors decide to have new ontology some time in the future. For the timebeing, this is the files as delivered. "

Are you ditching the 2.0 structure?

Bye for now
Jason.

That's what it says .

That is the lastest information from our RDF Changelog. As the entry notes, the current thinking is to keep our current ontology for now. As suggested you can periodically check that file to see if anything changes in the future.

Hi There,

Alright, I'm man enough to admit I don't know what 'ontology' means but Wikipedia has it as "Ontology is the philosophical study of the nature of being, existence or reality in general, as well as the basic categories of being and their relations." Isn't that overstating it a bit!?

Why did you drop your 2.0 ontology?

Bye for now
Jason.

Ontology with respect to DMOZ just means the structure of our categories and how they are linked together.

Why did you drop your 2.0 ontology?

Click to expand...

All the publically available information is available in that changelog file. Anything else would be considered internal information.

I fought a losing battle to say "taxonomy" or "taxonomic structure" instead. You may be one of those rare people who find that term clearer.

One approach would be to merge the ontology’s into a single graph and run an inference engine over them. For example, using Jena you'd create an inference model with your existing ontology, then create a second model with the external ontology. Add the second model to your inference model, and you've now got an enriched model that could very well return new results when queried.
Of course, if there's no overlap of resources, then you need to do a bit of extra work to integrate the models. Some people use NLP techniques to generate bridging facts, such as using Word Net to identify synonymy and generate facts that use owl: sameAs and its ilk. Other people prefer defining rules that conclude bridging facts; for example, concluding foaf: knows from family relationships.