Question answering based on pervasive agent ontology and Semantic Web

Semantic Web technologies bring new benefits to knowledge-based question answering system. Especially, ontology is becoming the pivotal methodology to represent domain-specific conceptual knowledge in order to promote the semantic capability of a QA system. In this paper we present a QA system in which the domain knowledge is represented by means of ontology. In addition, personalized services are enabled through modeling users' profiles in the form of pervasive agent ontology, and a Chinese Natural Language human-machine interface is implemented mainly through a NL parser in this system. An initial evaluation result shows the feasibility to build such a semantic QA system based on pervasive agent ontology, the effectivity of personalized semantic QA, the extensibility of pervasive agent ontology and knowledge base, and the possibility of self-produced knowledge-based on semantic relations in the pervasive agent ontology.

[1]  D. Lindberg,et al.  Unified Medical Language System , 2020, Definitions.

[2]  Alexa T. McCray,et al.  Concepts, Issues, and Standards. Current Status of the NLM's Umls Project: The Scope and Structure of the First Version of the UMLS Seoantic Network , 1990 .

[3]  R. Durbin,et al.  The Sequence Ontology: a tool for the unification of genome annotations , 2005, Genome Biology.

[4]  Jon Atle Gulla,et al.  Natural language analysis for semantic document modeling , 2001, Data Knowl. Eng..

[5]  D. Lindberg,et al.  The Unified Medical Language System , 1993, Methods of Information in Medicine.

[6]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[7]  Ellen M. Voorhees,et al.  The TREC-8 Question Answering Track , 2001, LREC.

[8]  Mark T. Maybury.  New Directions in Question Answering , 2004 .

[9]  Jon Atle Gulla,et al.  Natural Language Analysis for Semantic Document Modeling , 2000, NLDB.

[10]  Takahiro Kawamura,et al.  Semantic Matching of Web Services Capabilities , 2002, SEMWEB.

[11]  Patrick Lambrix,et al.  Evaluation of ontology development tools for bioinformatics , 2003, Bioinform..

[12]  Thomas C. Rindflesch,et al.  Semantic representation of consumer questions and physician answers , 2006, Int. J. Medical Informatics.

[13]  Michael Collins,et al.  A New Statistical Parser Based on Bigram Lexical Dependencies , 1996, ACL.

[14]  Paulo Pinheiro da Silva,et al.  Trusting Answers on the Web , 2003 .

[15]  Shichao Zhang,et al.  Mining Multiple Data Sources: Local Pattern Analysis , 2006, Data Mining and Knowledge Discovery.