The LogAnswer system is an application of automated reasoning to the field of open domain question answering. In order to find answers to natural language questions regarding arbitrary topics, the system integrates an automated theorem prover in a framework of natural language processing tools. The latter serve to construct an extensive knowledge base automatically from given textual sources, while the automated theorem prover makes it possible to derive answers by deductive reasoning. In the paper, we discuss the requirements to the prover that arise in this application, especially concerning efficiency and robustness. The proposed solution rests on incremental reasoning, relaxation of the query (if no proof of the full query is found), and other techniques. In order to improve the robustness of the approach to gaps of the background knowledge, the results of deductive processing are combined with shallow linguistic features by machine learning.
Abstract. LogAnswer is a logic-oriented question answering system developed by the AI research group at the University of Koblenz-Landau and by the IICS at the University of Hagen. The system addresses two notorious problems of the logic-based approach: Achieving robustness and acceptable response times. Its main innovation is the use of logic for simultaneously extracting answer bindings and validating the corresponding answers. In this way the inefficiency of the classical answer extraction/answer validation pipeline is avoided. The prototype of the system, which can be tested on the web, demonstrates response times suitable for real-time querying. Robustness to gaps in the background knowledge and errors of linguistic analysis is achieved by combining the optimized deductive subsystem with shallow techniques by machine learning.
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