Joint Entity-Relation Extraction from Wheat Variety Certification Texts for Knowledge Graph Construction and Variety Relationship Analysis

The certification information of wheat varieties contains valuable breeding knowledge and plays an important role in germplasm resource management and breeding research. However, most certification information is stored in unstructured text form, making it difficult to support efficient knowledge acquisition and utilization. To address this issue, this paper investigates joint entity-relation extraction from wheat variety certification texts and its application to knowledge graph construction. Specifically, building on existing character-word fusion methods, we propose the Joint Entity-Relation Extraction for Wheat Variety Certification Texts (JERE-WVCT) to address indiscriminate incorporation of candidate features in character-word fusion that obscures key features, a lack of differentiated weight assignment for features, and severe imbalance across relation types. Within JERE-WVCT, a deep character-word fusion mechanism based on hierarchical filtering and ranking is designed to enhance the representation of domain-specific entities. In addition, relation labels are incorporated as prior knowledge to alleviate the impact of relation type imbalance and improve the model’s triple extraction capability. Experimental results show that JERE-WVCT achieves an F1 score of 96.78% on the wheat variety certification corpus, outperforming all baseline models. Based on the extracted triples, a wheat variety knowledge graph is constructed, and exploratory variety relationship analysis is conducted as a downstream application. The results demonstrate the effectiveness of the proposed model for structured knowledge acquisition and support graph-based exploration of wheat variety information.