Discover a tool that provides ontology analytics over highly expressive ontologies. The Scalable Highly Expressive Reasoner allows and reveals logical inconsistencies in the data, helps you eliminate these irregularities before issuing semantic queries and explains why a specific result set is an answer to the query.
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Member since:
2007-12-26
Member since:2008-07-02
Member since:2006-06-03
Don't feel stupid 
. The problem is just that the paper is using a lot of technical jargon without explaining it properly. For a much clearer overview of what is going on, see:
http://en.wikipedia.org/wiki/Web_Ontology_Language
or
http://www.w3.org/TR/owl-guide/
Basically, it's an automated theasaurus on steroids. You build a database of things(classifiers) that you know about objects, then the computer can use it to infer things about other objects. So, for example. If you tell it that:
Chardonnay is an alcoholic drink
Chardonnay is made from grapes
Wine is an alcoholic drink
Wine is made from grapes
Then, if you tell your semantic-web engine to search for wines, when it comes across a Chardonnay listed, it will be able to infer that it is a wine without you actually telling it that. Of course that is a very basic example. Another good example is searching a drug database to infer wether 2 drugs might have side-effects when taken together, based on incomplete data.
As far as I can tell, what is special about the IBM approach, is that it uses 2 algorithms to resolve relationships.
1. A fast algorithm quickly tries to eliminate whole sections of the classifier database by asking special 'aggregations' about the query. so, if the results of asking the Ciders class 'Is a Wine?' and 'Is not a Wine?' are identical, then all Ciders can be eliminated from the query.
2. Then a full, traditional algorithm is used on the subset of classes that cannot be eliminated.
