- From: Tim Finin <finin@cs.umbc.edu>
- Date: Tue, 18 Nov 2008 08:12:29 -0500
- To: John Goodwin <John.Goodwin@ordnancesurvey.co.uk>
- CC: Ian Davis <me@iandavis.com>, Tim Berners-Lee <timbl@w3.org>, Chris Bizer <chris@bizer.de>, public-lod@w3.org, Semantic Web <semantic-web@w3.org>, dbpedia-discussion@lists.sourceforge.net, dbpedia-announcements@lists.sourceforge.net, Tom Briggs <thb1@umbc.edu>, Yun Peng <ypeng@cs.umbc.edu>
This is an interesting discussion. By coincidence, yesterday Tom Briggs [1] defended his dissertation [2] on 'Constraint Generation and Reasoning in OWL' which was done with Professor Yun Peng [3]. He started with an analysis of Swoogle's data that showed that 75% of published Semantic Web properties have neither domain or range constraints and evaluated algorithms for inferring them. Rather than focusing on instance data, he looked at what could be learned from how the properties were used in the TBOX, e.g., for specifying role restrictions. He has a paper on this that he has submitted to a conference and should finish revising his dissertation in the next few weeks. Here is the abstract for his defense: Constraint Generation and Reasoning in OWL Thomas H. Briggs The majority of OWL ontologies in the emerging Semantic Web are constructed from properties that lack domain and range constraints. Constraints in OWL are different from the familiar uses in programming languages and databases, and are actually type assertions that are made about the individuals which are connected by the property. These assertions can add vital information to the model because they are assertions of type on the individuals involved, and they can also give information on how the defining property may be used. Three different automated generation techniques are explored in this research: disjunction, least-common named subsumer, and vivification. Each algorithm is compared for the ability to generalize, and the performance impacts with respect to the reasoner. A large sample of ontologies from the Swoogle repository are used to compare real-world performance of these techniques. Finally, using generated facts, a type of default reasoning, may conflict with future assertions to the knowledge base. While general default reasoning is non-monotonic and undecidable a novel approach is introduced to support efficient retraction of the default knowledge. Combined, these techniques enable a robust and efficient generation of domain and range constraints which will result in inference of additional facts and improved performance for a number of Semantic Web applications. [1] http://ebiquity.umbc.edu/person/html/Tom/Briggs/ [2] http://ebiquity.umbc.edu/event/html/id/273/ [3] http://ebiquity.umbc.edu/person/html/Yun/Peng/ -- Tim Finin, Computer Science & Electrical Engineering, Univ of Maryland Baltimore County, 1000 Hilltop Cir, Baltimore MD 21250. finin@umbc.edu http://umbc.edu/~finin 410-455-3522 fax:-3969 http://ebiquity.umbc.edu
Received on Tuesday, 18 November 2008 13:13:45 UTC