摘要
随着知识图谱在人工智能领域的发展,对不同源的知识图谱进行融合,以得到覆盖范围更广的知识图谱的需求日益增加。本体作为知识图谱的上层结构,对知识图谱的构建具有指导作用。为了解决知识图谱融合中本体对齐的问题,文中提出了基于自注意力模型融合多维相似度的方法,从而提高本体对齐的精度。首先,对来自两个本体的概念进行基于字符串的、基于语义的和基于结构信息的多维度相似性度量;然后,使用自注意力模型对上述多种相似度度量结果进行融合,进而判断是否相似并进行对齐。在公开数据集上进行实验,实验结果表明,相比现有的本体对齐方法,所提方法通过聚合多维度的相似性特征能够得到更优的对齐结果。
With the development of knowledge graph in the field of artificial intelligence, there is an increasing demand to integrate knowledge graph from different sources to obtain a big knowledge graph with wider coverage.Ontology is the superstructure that can guide the construction of knowledge graph.To solve the problem of ontology alignment in knowledge graph fusion, this paper proposes an ontology alignment method based on self-attention model to combine multidimensional similarities.Firstly, two concepts from two ontologies are multi-dimensional measured by string-based, semantic-based and structure-based similarities.Then, self-attention model is used to combine above similarity calculations to judge whether the two concepts are similar or not and align them.Experiments on public datasets show that, compared with existing ontology alignment methods, the proposed method can obtain better alignment results by aggregating multi-dimensional similarity features.
作者
吴子仪
李邵梅
姜梦函
张建朋
WU Zi-yi;LI Shao-mei;JIANG Meng-han;ZHANG Jian-peng(National Digital Switching System Engineering&Technological R&D Center,Zhengzhou 450002,China)
出处
《计算机科学》
CSCD
北大核心
2022年第9期215-220,共6页
Computer Science
基金
国家自然科学基金青年科学基金(62002384)
郑州市协同创新重大专项(162/32410218)。
关键词
知识图谱融合
本体对齐
相似度计算
自注意力模型
Knowledge graph fusion
Ontology alignment
Similarity calculation
Self-attention model
