To: | Ontology Summit 2011 discussion <ontology-summit@xxxxxxxxxxxxxxxx> |
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From: | Ali SH <asaegyn+out@xxxxxxxxx> |
Date: | Mon, 2 Apr 2012 15:34:29 -0400 |
Message-id: | <CADr70E2S46ump0gp5PCkYed=7k13JYmgTXiA=b0Az+0tKt0sjQ@xxxxxxxxxxxxxx> |
Hello all, I want to direct some attention to this segment on the Big Data Challenges synthesis page.
As noted in last Friday's organizing committee, this section may elicit a lot of comments. We should address this prior to the cut-off for Communique revisions. My interpretation of the above is that the claim is contextually. It is certainly true that in some cases a small amount of machine readable semantics can go a long way. As noted on bullet (38G7), it really seems to depend on the target application and the underlying value proposition that drives the creation or application of computational ontology to the problem space.
The wording as above focuses on the negatives of increased expressivity, which imo is less constructive than perhaps highlighting the fact that it really should be the intended application and purpose of the ontology that drives the level of required expressivity. Most of the points above would then apply to only those cases where the value proposition and intended ontology applications really only do require limited expressivity.
Indeed, Leo's slides from the 2007 summit, esp. 20, 25 & 26 say pretty much the same thing: http://ontolog.cim3.net/file/resource/presentation/LeoObrst_20060112/OntologySpectrumSemanticModels--LeoObrst_20060112.ppt
What do others think? Best, Ali
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