在知识图谱实体关系预测中对DistMult解码器的优化研究  

Study on DistMult Decoder in Knowledge Graph Entity Relationship Prediction

作　　者：韩以健 王宝会[1] HAN Yijian;WANG Baohui(School of Software,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学软件学院,北京100191

出　　处：《计算机科学》2024年第S02期21-25,共5页Computer Science

摘　　要：国家电网甘肃电力科学院希望通过大量科研文献构建电力行业知识图谱,并深度挖掘知识图谱中的的潜在关联。关系预测模型是解决这类问题的关键技术,也是知识图谱中的重要技术,是近年来科研工作者的研究热点。大量论文和实验已经证明使用编码器加解码器组合的框架在关系预测任务中有不错的表现。在这种框架下,由于图神经网络技术的进步,近年来有有不少工作通过以图神经网络为编码器并加以优化的方案来提升关系预测的效果,而忽略了解码器的作用。受到余弦相似度的启发,提出了基于DistMult的新型解码器COS-DistMult,并在真实的数据集上进行对比实验。实验结果表明,关系预测模型的评价指标Hits@10的值提高了2%左右,证明在以编码器加解码器为框架的关系预测任务中,优化解码器结构是一种行之有效的方法。State Grid Gansu Electric Power Academy hopes to construct a knowledge graph of the power industry through a large amount of scientific research literature and deeply explore the potential correlations in the knowledge graph.The relationship prediction model is a key technology for solving such problems and an important technology in knowledge graphs,which has been a research hotspot for researchers in recent years.A large number of papers and experiments have demonstrated that the framework combining encoder and decoder performs well in relation prediction tasks.Under this framework,due to the advancement of graph neural network technology,there have been many works in recent years that have improved the performance of relationship prediction by using graph neural networks as encoders and optimizing them,while neglecting the role of decoders.Taking inspiration from cosine similarity,this paper proposes a novel decoder COS DistMult based on DistMult and conducts comparative experiments on real datasets,and the experimental results indicate that the evaluation indicator Hits@10of the relationship prediction model increases by 2%.It is proves that optimizing the decoder structure is an effective method in relation prediction tasks based on an encoder decoder framework.

关 键 词：知识图谱 图神经网络 关系预测 DistMult解码器 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]