一种融合实体关联性约束的表示学习方法  

A Representation Learning Method of Fusing Entity Affinity Constraints

作　　者：刘琼昕 马敬[2] 郑培雄 LIU Qiong-xin;MA Jing;ZHENG Pei-xiong(Beijing Engineering Applications Research Center on High Volume Language Information Processing and Cloud Computing,Beijing 100081,China;School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China;College of Computer Science and Technology,Harbin Engineering University,Harbin,Heilongjiang 150001,China)

机构地区：[1]北京市海量语言信息处理与云计算应用工程技术研究中心,北京100081 [2]北京理工大学计算机学院,北京100081 [3]哈尔滨工程大学计算机学院,黑龙江哈尔滨150001

出　　处：《北京理工大学学报》2020年第1期90-97,共8页Transactions of Beijing Institute of Technology

基　　金：国家部委预研项目(31511090201)

摘　　要：知识图谱的表示学习方法将实体和关系嵌入到低维连续空间中,从而挖掘出实体间的隐含联系.传统的表示学习方法多基于知识图谱的结构化信息,没有充分利用实体的描述文本信息.目前基于文本的表示学习方法多将文本向量化,忽略了文本中实体间的语义关联.针对上述缺点提出一种利用实体描述文本进行增强学习的方法,基于文本挖掘出关联性实体并对关联性进行分级,将关联性作为辅助约束融合到知识图谱的表示学习中.实验结果表明,该辅助约束能明显提升推理效果,优于传统的结构化学习模型以及基于深度学习的文本和结构的联合表示模型.Representation learning on knowledge graph aims to project both entities and relations into a low-dimensional continuous space and dig out the hidden relations between two entities.Traditional method does not make full use of entity’s description text and most of representation learning methods based on entity description project text into vector space without considering the relevance of entities in text.In this paper,a knowledge graph representation learning method was proposed,taking the advantage of entity description based on the traditional structure-based representation learning.In this method,the different relevant entities extracted based on entities description and relevant entities were fused as supplementary constraints information to knowledge graph representation learning.Experimental results on real world datasets show that,this method can enhance the inference effectiveness and outperforms structure-based representation learning method,especially outperform deep convolutional neural model which encode semantics of entity descriptions into structure-based representation learning.

关 键 词：知识图谱 表示学习 关联性 辅助约束 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]