融合迭代式关系图匹配和属性语义嵌入的实体对齐方法  

Entity Alignment Method Combining Iterative Relationship Graph Matching and Attribute Semantic Embedding

作　　者：迟棠 车超[1] CHI Tang;CHE Chao(Key Laboratory of Advanced Design and Intelligent Computing,Ministry of Education,Dalian University,Dalian 116622,China)

机构地区：[1]大连大学先进设计与智能计算省部重点实验室,大连116622

出　　处：《计算机科学》2023年第S02期69-74,共6页Computer Science

基　　金：国家自然科学基金(62076045,62102058);辽宁省教育厅服务地方项目(揭榜挂帅)(LJKFZ20220290);大连大学学科交叉项目(DLUXK-2023-YB-003,DLUXK-2023-YB-009)。

摘　　要：实体对齐是知识融合中的关键步骤,用于解决多源知识图谱中实体冗余、指代不明等问题。目前,大多数的实体对齐方法主要依赖于邻域网络,而忽略了关系间的连通以及属性信息,导致模型无法捕捉到复杂关系,额外信息也没有被充分利用。针对上述问题,提出一种迭代式关系图匹配和属性语义嵌入的实体对齐方法,将〈头实体,关系,尾实体〉进行转置,生成〈头关系,实体,尾关系〉构建,与实体图相对应的关系图,接着利用注意力机制编码实体和关系表示,二者通过相互迭代,能够更好地表示实体,再融合属性表示最终判定两个实体是否对齐。实验结果表明,本模型在DBP15K 3个跨语言数据集中显著优于其他6种方法,相比于最好方法Hit@1指标提升了4%,证明了关系匹配和属性语义的有效性。Entity alignment is a key step in knowledge fusion,which is used to solve the problem of entity redundancy and unknown reference in multi-source knowledge graph.At present,most of the entity alignment methods mainly rely on the neighborhood network,but ignore the connectivity and attribute information between the relationships.As a result,the model cannot capture the complex relationships,and the additional information is not fully utilized.To solve the above problems,an entity alignment method based on iterative graph reasoning and attribute semantic embedding is proposed.The〈head,relation,tail〉is transposed to generate〈head,relation,tail〉to construct the corresponding relationship graph with the entity graph,and then the attention mechanism is used to encode the entity and relation representation.The two can represent the entity better through iteration.The refusion property indicates the final determination of whether the two entities are aligned.Experimental results show that this model is significantly superior to the other six methods in the three cross-language data sets of DBP15K,and the index increases by 4%compared with the best method Hit@1,which proves the effectiveness of relational reasoning and attribute semantics.

关 键 词：知识图谱 实体对齐 图神经网络 关系匹配 属性语义 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]