Jiakai Li, Jianpeng Hu*, Geng Zhang
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005
Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first… More >
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