Abstract: Aiming at  the problem of poor  performance of entity relationship extraction in current domain knowledge graphs, we proposed a research method for entity relationship extraction models oriented towards domain knowledge graphs. Firstly, we established an entity relationship extraction model consisting of an encoding and decoding module, an entity recognition module, and an entity relationship extraction module. In the entity relationship extraction model, a bidirectional long short-term memory neural network was used to encode text sentences, and the feature representation vectors of the encoded text sentences were input into a deep neural network-based entity recognition module for entity recognition of text sentences, and  the recognition results were input into the entity relationship extraction module based on convolutional neural networks for  entity relationship extraction. Secondly,  the entity relationship triplet obtained from entity relationship extraction was input into the encoding and decoding module for decoding operation, achieving the final entity relationship extraction for domain oriented knowledge graph. The experimental results show that the proposed method has better entity relationship extraction effect and overall application effect.

Key words: knowledge graph, entity relationship extraction, entity recognition, convolutional neural network