Chinese named entity relation extraction for enterprise knowledge graph construction

doi:10.3969/j.issn.1000-5641.2018.03.007

Abstract

Abstract: The enterprise knowledge graph is a kind of domain knowledge base for the financial field to describe business relationships between enterprises. Although the domain knowledge graph is not broadly covered in the field, the precision of the knowledge is better than with an open knowledge graph. Despite the fact that open knowledge graphs have made significant advancements in recent years, vertical fields-especially business-have not seen in-depth applications in practice; this has resulted in significant demands on the enterprise knowledge graph. This paper proposes a Chinese entity relation extraction method based on classification for the limitation of extraction results. In this method, the maximum entropy model is used to analyze the data of selected companies' announcements to determine the optimal feature template. The results show that accuracy rates reach over 85% in the enterprise bulletin data set.

Key words: enterprise knowledge graph, named entity relation extraction, maximum entropy

CLC Number:

TP391

SUN Chen, FU Ying-nan, CHENG Wen-liang, QIAN Wei-ning. Chinese named entity relation extraction for enterprise knowledge graph construction[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(3): 55-66.

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