Combining embedding-based and symbol-based methods for entity alignment. (April 2022)

• Record Type: Journal Article
• Authors: Jiang, Tingting; Bu, Chenyang; Zhu, Yi; Wu, Xindong
• Is Part Of: Pattern recognition. Volume 124(2022)
• Publication Date: 2022-04
• Subjects: Entity alignment -- Knowledge graph embedding -- String Similarity; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108433
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a two-stage framework for entity alignment from the perspective of combining the advantages of both symbol-based and embedding-based methods. A series of symbol-based methods are adopted to align the relation pairs in stage I. Symbol-based methods and a hybrid embedding model are combined to match the entity pairs in stage II. Experimental results from real-word datasets demonstrate that our proposed method is effective. Ablation studies illustrate our proposed strategies are versatile and can also be applied to other embedding models. Abstract: The objective of entity alignment is to judge whether entities refer to the same object in the real world. Methods for entity alignment can be grossly divided into two groups: conventional symbol-based entity alignment methods and embedding-based entity alignment methods. Both groups of methods have advantages and disadvantages (which are detailed in Section 1). Therefore, combining the advantages of both methods might be a promising strategy. However, to the best of our knowledge, only the RTEA algorithm that was proposed in our previous conference paper (Proceeding of Pacific Rim International Conference on Artificial Intelligence, pp. 162–175, 2019) utilizes this strategy for entity alignment. This manuscript is an extended version of that conference paper, in which an improved algorithm, namely, ESEA (combining e mbedding-based and s ymbol-based methods for e ntity a lignment), is proposed based on the following steps. First, a novel method for combining embedding models with symbol-based models is proposed. Entities with high vector similarities are obtained through a hybrid embedding model, and the final aligned entity pairs are calculated via symbol-based methods. Second, a series of symbol-based methods, instead of only the edit distance method in the original version, are combined with embedding-based methods for relation alignment. Third, we combine symbol-based and embedding-based methods in a more complicated framework with the objective of better exploiting the advantages of both methods. The experimental results on real-world datasets demonstrate that the proposed method outperformed several state-of-the-art embedding-based entity alignment approaches and outperformed our previous RTEA method. Graphical abstract: An example of fusing multiple financial knowledge graphs (KGs) from different sources. Through aligning and integrating multi-source heterogeneous data, we could observe that Jack Ma (also called Mr. Ma) is not only the principal founder of the Alibaba Group, but also a director of Softbank. Therefore, a more complete knowledge graph with rich information can be obtained, which is essential for applications such as financial search and financial question answering Image, graphical abstract .