Joint Entity-Relation Extraction for Knowledge Graph Construction in Marine Ranching Equipment

The construction of marine ranching is a crucial component of China’s Blue Granary strategy, yet the fragmented knowledge system in marine ranching equipment impedes intelligent management and operational efficiency. This study proposes a novel knowledge graph (KG) framework tailored for marine ranching equipment, integrating hybrid ontology design, joint entity-relation extraction, and graph-based knowledge storage: (1) The limitations in existing KG are obtained through targeted questionnaires for diverse users and employees; (2)A domain ontology was constructed through a combined top-down and bottom-up approach, defining seven core concepts and eight semantic relationships; (3) Semi-structured data from enterprises and standards, combined with unstructured data from literature were systematically collected, cleaned via Scrapy and regular expression, and standardized into JSON format, forming a domain-specific corpus of 1,456 annotated sentences; (4)A novel BERT-BiGRU-CRF model was developed, leveraging contextual embeddings from BERT, parameter-efficient sequence modeling via BiGRU, and label dependency optimization using CRF. The TE+SE+Ri+BMESO tagging strategy was introduced to address multi-relation extraction challenges by linking theme entities to secondary entities; (4)The Neo4j-based knowledge graph encapsulated 2,153 nodes and 3,872 edges, enabling scalable visualization and dynamic updates. Experimental results demonstrated superior performance over BERT-BiLSTM-CRF, achieving 86.58% precision, 77.82% recall, and 81.97% F1-score(1.94% improvement, p < 0.05) . This study not only pioneers the first structured KG framework for marine ranching equipment but also offers a transferable methodology for vertical domain knowledge extraction.