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

["Matthew West" <dr.matthew.west@xxxxxxxxx>]

Dear Ali,

 

I share your concerns on this. See some comments below.

 

 

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)

MW:  I think you should not limit yourself to what you can say, but you might want to limit yourself to what you try to reason over. There are other things you can do with data apart from reasoning (like just look at it) and not even to allow things to be said is just unhelpful (unless it is logically invalid to say it of course).

* Computationally expensive    (38G8)

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

MW: Again this is only true when you are trying to reason and what you are trying to reason over. The sensible approach (~in my view) would be to allow expressivity, but restrict the subsets over which reasoning is done. For different problems different subsets are likely to be appropriate, and with big data there are likely to be more than one problem for which the overall dataset is applicable.

* 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)

MW: Again, this is about the reasoning being attempted. You could easily prevent practical reasoning over subsets of the data if you restrict what the data can be in the first place based on this kind of principle.

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

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

MW: Agreed. I actually think that most value of ontology in big data is enabling different data sets to be brought together by doing “semantic brokering” between the different (possibly implicit) ontologies of the source data sets.

 

Regards

 

Matthew West                           

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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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