Research on knowledge extraction in knowledge graph construction

Xiaoqing Xia; Xinchi Li; Hongpeng Chu; Kaicheng Zhang; Kang Liu

doi:10.1117/12.2660008

3 February 2023 Research on knowledge extraction in knowledge graph construction

Xiaoqing Xia, Xinchi Li, Hongpeng Chu, Kaicheng Zhang, Kang Liu

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111L (2023) https://doi.org/10.1117/12.2660008
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Knowledge Graphs (KGs) are composed of structured information in the form of entities and relations. And the process of extracting entities and relations from data is called Knowledge Extraction. Knowledge extraction is a fundamental task in the field of Natural Language Processing (NLP) and a key part of knowledge graph construction. In this paper, we provide comprehensive research on knowledge extraction in knowledge graph construction. We first introduce the technical architecture of the KGs and the classification of knowledge extraction. Then, we systematically categorize existing works based on the development of knowledge extraction. Finally, we review current open-source tools for knowledge extraction and summarize their advantages and disadvantages.

Citation Download Citation

Xiaoqing Xia, Xinchi Li, Hongpeng Chu, Kaicheng Zhang, and Kang Liu "Research on knowledge extraction in knowledge graph construction", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111L (3 February 2023); https://doi.org/10.1117/12.2660008

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