Towards overcoming the knowledge acquisition bottleneck in answer set prolog applications: embracing natural language inputs

Authors:
Chitta Baral;Juraj Dzifcak;Luis Tari
Affiliations:
School of Computing and Informatics, Arizona State University, Tempe, AZ;School of Computing and Informatics, Arizona State University, Tempe, AZ;School of Computing and Informatics, Arizona State University, Tempe, AZ
Venue:
ICLP'07 Proceedings of the 23rd international conference on Logic programming
Year:
2007

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Abstract

Answer set Prolog, or AnsProlog in short, is one of the leading knowledge representation (KR) languages with a large body of theoretical and building block results, several implementations and reasoning and declarative problem solving applications. But it shares the problem associated with knowledge acquisition with all other KR languages; most knowledge is entered manually by people and that is a bottleneck. Recent advances in natural language processing have led to some systems that convert natural language sentences to a logical form. Although these systems are in their infancy, they suggest a direction to overcome the above mentioned knowledge acquisition bottleneck. In this paper we discuss some recent work by us on developing applications that process logical forms of natural language text and use the processed result together with AnsProlog rules to do reasoning and problem solving.