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Description logic programs: combining logic programs with description logic
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Source International World Wide Web Conference archive
Proceedings of the 12th international conference on World Wide Web table of contents
Budapest, Hungary
SESSION: Foundations of the semantic web table of contents
Pages: 48 - 57  
Year of Publication: 2003
ISBN:1-58113-680-3
Authors
Benjamin N. Grosof  MIT Sloan School of Management, Cambridge, MA
Ian Horrocks  University of Manchester, Manchester, UK
Raphael Volz  University of Karlsruhe, Karlsruhe, Germany
Stefan Decker  USC, Los Angeles, CA
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 20,   Downloads (12 Months): 155,   Citation Count: 31
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ABSTRACT

We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR) contained within this intersection: Description Logic Programs (DLP), and the closely related Description Horn Logic (DHL) which is an expressive fragment of first-order logic (FOL). DLP provides a significant degree of expressiveness, substantially greater than the RDF-Schema fragment of Description Logic. We show how to perform DLP-fusion: the bidirectional translation of premises and inferences (including typical kinds of queries) from the DLP fragment of DL to LP, and vice versa from the DLP fragment of LP to DL. In particular, this translation enables one to "build rules on top of ontologies": it enables the rule KR to have access to DL ontological definitions for vocabulary primitives (e.g., predicates and individual constants) used by the rules. Conversely, the DLP-fusion technique likewise enables one to "build ontologies on top of rules": it enables ontological definitions to be supplemented by rules, or imported into DL from rules. It also enables available efficient LP inferencing algorithms/implementations to be exploited for reasoning over large-scale DL ontologies.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  36
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Collaborative Colleagues:
Benjamin N. Grosof: colleagues
Ian Horrocks: colleagues
Raphael Volz: colleagues
Stefan Decker: colleagues

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