Deduction Engine Design for PNL-Based Question Answering System

Authors:
Zengchang Qin;Marcus Thint;M. M. Sufyan Beg
Affiliations:
Berkeley Initiative in Soft Computing, Computer Science Division, EECS Department, University of California, Berkeley CA 94720, USA and Computational Intelligence Group, Intelligent Systems Resear ...;Computational Intelligence Group, Intelligent Systems Research Centre, British Telecommunications (BT) Group,;Berkeley Initiative in Soft Computing, Computer Science Division, EECS Department, University of California, Berkeley CA 94720, USA and Computational Intelligence Group, Intelligent Systems Resear ...
Venue:
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Year:
2007

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Abstract

In this paper, we present a methodology for designing a Precisiated Natural Language (PNL) based deduction engine for automated Question Answering (QA) systems. QA is one type of information retrieval system, and is regarded as the next advancement beyond keyword-based search engines, as it requires deductive reasoning and use of domain/background knowledge. PNL, as discussed by Zadeh, is one representation of natural language based on constraint-centered semantics, which is convenient for computing with words. We describe a hybrid reasoning engine which supports a "multi-pipe" process flow to handle PNL-based deduction as well as other natural language phrases that do not match PNL protoforms. The resulting process flows in a nested form, from the inner to the outer layers: (a) PNL-based reasoning where all important concepts are pre-defined by fuzzy sets, (b) deduction-based reasoning which enables responses drawn from generated/new knowledge, and (c) key phrase based search when (a) and (b) are not possible. The design allows for two levels of response accuracy improvement over standard search, while retaining a minimum performance level of standard search capabilities.