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Querying Knowledge via Multi-Hop English Questions

Published online by Cambridge University Press:  20 September 2019

TIANTIAN GAO Open the ORCID record for TIANTIAN GAO [Opens in a new window] ,
PAUL FODOR  and
MICHAEL KIFER
TIANTIAN GAO
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: tiagao@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
PAUL FODOR
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: tiagao@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
MICHAEL KIFER
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: tiagao@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
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Abstract

The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.

Keywords

Controlled Natural LanguageKnowledge RepresentationReasoningQuestion Answering
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 636 - 653
Copyright
© Cambridge University Press 2019 

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