Re: [ontology-summit] Clarification re Big Data Challenges Synthesis

[Todd J Schneider <todd.schneider@xxxxxxxxxxxx>]

Ali,

One solution is not to make (explicit) reference to these
assertions in the communique. Failing to make reference
shouldn't diminish the content of the communique.

Todd

Ali SH ---04/02/2012 03:35:58 PM---Hello all, I want to direct some attention to this segment on the Big Data Challenges

From: Ali SH <asaegyn+out@xxxxxxxxx>
To: Ontology Summit 2011 discussion <ontology-summit@xxxxxxxxxxxxxxxx>
Date: 04/02/2012 03:35 PM
Subject: [ontology-summit] Clarification re Big Data Challenges Synthesis
Sent by: ontology-summit-bounces@xxxxxxxxxxxxxxxx

---

Hello all,

I want to direct some attention to this segment on the Big Data Challenges synthesis page.

http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2012_BigDataChallenge_Synthesis#nid38G5

3. Many times people try to have both expressivity and scale. This is very expensive    (38G5)

* Don’t be seduced by expressivity    (38G6)

* Just because you CAN say it doesn’t mean you SHOULD say it. Stick to things that are strictly useful to building your big data application.    (38G7)

* Computationally expensive    (38G8)

* Expressivity is not free. It must be paid for either with load throughput or query latency, or both.    (38G9)

* Not easily partitioned    (38GA)

* Higher expressivity often involves more than one piece of information from the abox – meaning you have to cross server boundaries. With lower expressivity you can replicate the ontology everywhere on the cluster and answer questions LOCALLY.    (38GB)

* A little ontology goes a long way    (38GC)

* There can be a lot of value just getting the data federated and semantically aligned.    (38GD)

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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