文章基本信息

标题：ILP-based Inference for Cost-based Abduction on First-order Predicate Logic
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作者：Naoya Inoue ; Kentaro Inui
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2014
卷号：9
期号：1
页码：83-110
DOI：10.11185/imt.9.83
出版社：Information and Media Technologies Editorial Board
摘要：Abduction is desirable for many natural language processing (NLP) tasks. While recent advances in large-scale knowledge acquisition warrant applying abduction with large knowledge bases to real-life NLP problems, as of yet, no existing approach to abduction has achieved the efficiency necessary to be a practical solution for large-scale reasoning on real-life problems. In this paper, we propose an efficient solution for large-scale abduction. The contributions of our study are as follows: (i) we propose an efficient method of cost-based abduction in first-order predicate logic that avoids computationally expensive grounding procedures; (ii) we formulate the best-explanation search problem as an integer linear programming optimization problem, making our approach extensible; (iii) we show how cutting plane inference, which is an iterative optimization strategy developed in operations research, can be applied to make abduction in first-order logic tractable; and (iv) the abductive inference engine presented in this paper is made publicly available.
关键词：Cost-based Abduction;Integer Linear Programming;Discourse Processing;Interpretation as Abduction