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• W2080109481 abstract "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 largescale 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 bestexplanation 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." @default.
• W2080109481 created "2016-06-24" @default.
• W2080109481 creator A5028046901 @default.
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• W2080109481 date "2013-12-13" @default.
• W2080109481 modified "2023-09-26" @default.
• W2080109481 title "ILP-based Inference for Cost-based Abduction on First-order Predicate Logic" @default.
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• W2080109481 doi "https://doi.org/10.11185/imt.9.83" @default.
• W2080109481 hasPublicationYear "2013" @default.
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