Link Prediction Method Based on Sub-graph Feature Fusion
Link prediction aims to predict missing fact triplets in the knowledge graph query process,and is commonly used in tasks such as intelligent question answering and information retrieval.However,due to the large number of nodes and relationships in the knowledge graph,encoding the entire graph requires significant resources,and the encoding method of graph embedding lacks the semantic information inherent in the query sentence,resulting in unsatisfactory link prediction results.To this end,a subgraph embedding based entity linking method LPBS is proposed.Based on reinforcement learning models,relevant strategies are designed to obtain the upper and lower text sets of predicted link paths and merge them for input encoding.Then,the embedding features of query sentences and subgraphs are obtained through a dual tower model based on multi head self attention mechanism.Finally,the quantitative features are fused through cross attention mechanism to obtain the predicted distribution of each node.Testing on a self built industrial dataset found that the proposed method achieved an MMR of 0.362,Hits@1 reached 0.313 and demonstrated the effectiveness of the model through ablation experiments.
link predictionreinforcement learningmulti-head self-attention mechanismdouble-tower modelcross-attention mecha-nism
国家科技期刊平台
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