Research on equipment entity recognition and attribute extraction for knowledge graph construction

Yun Cheng; Qian Liu; Chao Jiang

doi:10.1117/12.3012574

21 December 2023 Research on equipment entity recognition and attribute extraction for knowledge graph construction

Yun Cheng, Qian Liu, Chao Jiang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701I (2023) https://doi.org/10.1117/12.3012574
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Equipment entity recognition and attribute extraction are the basis of constructing equipment knowledge graph. In this paper, we firstly design a model framework for equipment entity recognition and its attribute extraction. Then, combining the advantages of BiLSTM and CRF, an equipment entity recognition method based on Dic+BiLSTM-CRF is proposed by constructing a domain-specific dictionary for equipment. Furthermore, the equipment entity attribute extraction method is designed based on HMM model and Viterbi algorithm. The experiment results show that compared with the traditional methods, the performance of equipment entity recognition based on Dic+BiLSTM-CRF is close to the general domain entity recognition level. The accuracy rate, recall rate and F1 value of equipment entity attribute extraction are higher than 80%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yun Cheng, Qian Liu, and Chao Jiang "Research on equipment entity recognition and attribute extraction for knowledge graph construction", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701I (21 December 2023); https://doi.org/10.1117/12.3012574

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