软件导刊2024，Vol.23Issue(7)：58-63,6.DOI:10.11907/rjdk.231508
基于子图特征融合的链接预测方法
Link Prediction Method Based on Sub-graph Feature Fusion
滕磊 1田炜 1靖琦东 1李霜 1李倩1
作者信息
- 1. 中电工业互联网有限公司,湖南 长沙 410000
- 折叠
摘要
Abstract
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.关键词
链接预测/强化学习/多头自注意力机制/双塔模型/交叉注意力机制Key words
link prediction/reinforcement learning/multi-head self-attention mechanism/double-tower model/cross-attention mecha-nism分类
信息技术与安全科学引用本文复制引用
滕磊,田炜,靖琦东,李霜,李倩..基于子图特征融合的链接预测方法[J].软件导刊,2024,23(7):58-63,6.
