Entity Extraction for Research and Development Project Knowledge Graph

Abstract

In the process of constructing a knowledge graph for research and develop (R&D) projects, entity extraction for R&D project abstracts helps to quickly understand project content and promote the application and transformation of research results. This article adopts a scientific entity recognition model based on BERT-IDCNN-CRF (BERT combined with Iterative Dilated Convolutional Neural Network and Conditional Random Field) for the entity extraction task in the construction of knowledge graph for R&D projects. This model uses BERT to extract semantic representations from abstract texts, and then uses IDCNN to deeply capture local and global features in the text. Combined with CRF to learn the context transfer relationship between text labels. Finally it achieved the precision of 69.3%, the recall of 65.8%, and the F1-score of 67.5% in scientific entity recognition. On the basis of this model, this article proposes a knowledge graph construction scheme for R&D projects, and constructs a knowledge graph centered on R&D projects, which includes 12 kinds of entities and 13 kinds of relationships.