一种新的基于压缩感知的图同构网络关键节点识别和实体对齐

IF 4.1 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal on Semantic Web and Information Systems Pub Date : 2022-04-01 DOI:10.4018/ijswis.315600
Wenbin Zhao, Jing Huang, Tongrang Fan, Yongliang Wu, Keqiang Liu
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引用次数: 2

摘要

近年来,实体对齐的相关研究主要集中在基于知识嵌入和图神经网络的实体对齐;然而,这些模型通常存在结构异质性和知识图谱的大规模问题。提出了一种基于图同构网络和压缩感知的实体对齐模型。首先,针对知识图谱的结构异构问题,在知识图谱中应用图同构网络编码器,捕获实体关系的结构相似性;其次,针对大规模问题,集成关键节点和社区进行优先实体对齐,提高执行速度。然而,现有的节点重要性排序算法无法准确识别知识图中的关键节点。因此,在节点重要性排序中采用压缩感知来提高关键节点的识别精度。作者进行了几个实验来测试所提出的实体对齐模型的效果和效率。
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A Novel Compressed Sensing-Based Graph Isomorphic Network for Key Node Recognition and Entity Alignment
In recent years, the related research of entity alignment has mainly focused on entity alignment via knowledge embeddings and graph neural networks; however, these proposed models usually suffer from structural heterogeneity and the large-scale problem of knowledge graph. A novel entity alignment model based on graph isomorphic network and compressed sensing is proposed. First, for the problem of structural heterogeneity, graph isomorphic network encoder is applied in knowledge graph to capture structural similarity of entity relation. Second, for the problem of large scale, key node and community are integrated for priority entity alignment to improve execution speed. However, the exiting node importance ranking algorithm cannot accurately identify key node in knowledge graph. So the compressed sensing is adopted in node importance ranking to improve the accuracy of identifying key node. The authors have carried out several experiments to test the effect and efficiency of the proposed entity alignment model.
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来源期刊
CiteScore
6.20
自引率
12.50%
发文量
51
审稿时长
20 months
期刊介绍: The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.
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