Constructing Patent Knowledge Graph with SpERT-Aggcn Model
[Objective]This paper proposes an information extraction model(SpERT-Aggcn)and constructs knowledge graphs for green cooperation patents based on this model.It helps us identify nested entities and improve the accuracy of relationship extraction for knowledge graphs.[Methods]First,we utilized the SpERT-Aggcn model to extract nested entities and relationships from patent abstracts.Then,we built an ontology using Protégé and mapped the triples with the constructed ontology.[Results]In relationship extraction,the SpERT-Aggcn model's F1 score was 2.61％higher than the SpERT model.The SpERT-Aggcn model's Fl score was 4.42％higher than the SpERT model for the long-distance relationship extraction tasks.The constructed knowledge graph for green cooperation patents contained 699,517 entities and 3,241,805 relationships.[Limitations]The Fl score of SpERT-Aggcn for extracting short-distance relationships was lower than the SpERT model,indicating a weaker capability of the proposed model in identifying short-distance relationships.[Conclusions]The proposed model could help us construct better knowledge graphs.
Green Cooperation PatentKnowledge GraphGraph Convolution NetworkInformation Extraction
万方数据
维普
