Open Access

ARTICLE

A Longest Matching Resource Mapping Algorithm with State Compression Dynamic Programming Optimization

Zhang Min, Teng Haibin, Jiang Ming, Wen Tao, Tang Jingfan

School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

* Corresponding Author: Zhang Min,

Intelligent Automation & Soft Computing 2019, 25(3), 625-635. https://doi.org/10.31209/2019.100000117

Abstract

Mapping from sentence phrases to knowledge graph resources is an important step for applications such as search engines, automatic question answering systems based on acknowledge base and knowledge graphs. The existing solution maps a simple phrase to a knowledge graph resource strictly or approximately from the text. However, it is difficult to detect phrases and map the composite semantic resource. This paper proposes a longest matching resource mapping scheme to solve this problem, namely, to find the longest substring in a sentence that can match the knowledge base resource. Based on this scheme, we propose an optimization algorithm based on state compression dynamic programming. Furthermore, we improve the operating efficiency by removing invalid states. Experimental results show that our proposed optimization algorithm considerably improves the efficiency of the benchmark algorithm in terms of running time.

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Keywords

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