Research on Relation Extraction of Academic Full-Text Based on Self-Owned Knowledge Enhancement

Zhuo Keqiu; Shen Si; Wang Dongbo

doi:10.13266/j.issn.0252-3116.2022.07.012

Library and Information Service >

2022 , Vol. 66 >Issue 7: 120 - 131

DOI: https://doi.org/10.13266/j.issn.0252-3116.2022.07.012

KNOWLEDGE ORGANIZATION

Research on Relation Extraction of Academic Full-Text Based on Self-Owned Knowledge Enhancement

Zhuo Keqiu ,
Shen Si ,
Wang Dongbo

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1. School of Information Management, Nanjing Agricultural University, Nanjing 210095;
2. School of Economics and Management, Nanjing University of Technology, Nanjing 210094

Received date: 2021-11-24

Revised date: 2022-01-19

Online published: 2022-04-15

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Abstract

[Purpose/Significance] Relation extraction under academic full-text is the key technology for the construction of academic full-text knowledge graph. The constructed academic knowledge graph can realize the structure and knowledge of documents, and improve the efficiency of researchers retrieving documents, analyzing documents and grasping scientific research trends, and cognitive reasoning through graphs contributes to implicit knowledge discovery.[Method/Process] Enhancing relation extraction through external knowledge has achieved results in many studies, but relation extraction for specific fields often lacked available external knowledge. The research in this paper found that the high-confidence knowledge in the full-text could also be used to assist the extraction of full-text relations. For this reason, based on the dual-system theory of cognitive processes (system 1 is intuitive cognition, system 2 is reasoning cognition), this paper designed a sentence-level model to acquire knowledge, and obtained high-confidence knowledge through remote supervision, and then high-confidence knowledge was integrated into the final classification layer of the text-level deep learning model.[Result/Conclusion] On the biomedical academic full-text data set (CDR-revised), the F1 is about 11.13% higher than the current state-of-the-art model.

Key words： academic full-text; relation extraction; self-owned knowledge enhancement; knowledge graph

Cite this article

Zhuo Keqiu , Shen Si , Wang Dongbo . Research on Relation Extraction of Academic Full-Text Based on Self-Owned Knowledge Enhancement[J]. Library and Information Service, 2022 , 66(7) : 120 -131 . DOI: 10.13266/j.issn.0252-3116.2022.07.012

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