论文信息 - Scalable Cleanup of Information Extraction Data Using Ontologies

Scalable Cleanup of Information Extraction Data Using Ontologies

The approach of using ontology reasoning to cleanse the output of information extraction tools was first articulated in SemantiClean. A limiting factor in applying this approach has been that ontology reasoning to find inconsistencies does not scale to the size of data produced by information extraction tools. In this paper, we describe techniques to scale inconsistency detection, and illustrate the use of our techniques to produce a consistent subset of a knowledge base with several thousand inconsistencies.

[1] P. Schönemann. On artificial intelligence , 1985, Behavioral and Brain Sciences.

[2] Edith Schonberg,et al. The Summary Abox: Cutting Ontologies Down to Size , 2006, SEMWEB.

[3] Richard Booth,et al. Knowledge Integration for Description Logics , 2005, AAAI.

[4] Raymond Reiter,et al. A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[5] Stefan Schlobach,et al. Diagnosing Terminologies , 2005, AAAI.

[6] Frank van Harmelen,et al. Reasoning with Inconsistent Ontologies , 2005, IJCAI.

[7] J. William Murdock,et al. Towards Knowledge Acquisition from Information Extraction , 2006, International Semantic Web Conference.

[8] Edith Schonberg,et al. Scalable Semantic Retrieval through Summarization and Refinement , 2007, AAAI.

[9] Aditya Kalyanpur,et al. Debugging and Repair of OWL Ontologies , 2006 .

[10] Dean Allemang,et al. The Semantic Web - ISWC 2006, 5th International Semantic Web Conference, ISWC 2006, Athens, GA, USA, November 5-9, 2006, Proceedings , 2006, SEMWEB.

[11] Bijan Parsia,et al. Pellet: An OWL DL Reasoner , 2004, Description Logics.