Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs (1908.08210v1)

Published 22 Aug 2019 in cs.CL

Abstract: Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

Authors (6)

Yuting Wu (22 papers)
Xiao Liu (402 papers)
Yansong Feng (81 papers)
Zheng Wang (400 papers)
Rui Yan (250 papers)
Dongyan Zhao (144 papers)

Citations (308)

View on Semantic Scholar

Summary

Insightful Overview of "Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs"

The paper "Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs" introduces a sophisticated method for aligning entities across different knowledge graphs (KGs), using a novel model termed the Relation-aware Dual-Graph Convolutional Network (RDGCN). The entity alignment problem, critical for integrating information from disparate KGs, involves identifying entities that refer to the same real-world entity but exist in separate KGs.

Methodology

The methodology proposed by the authors leverages a two-fold approach by utilizing dual graph convolutional networks. The RDGCN model is designed to effectively capture complex relational information that is frequently overlooked in entity alignment procedures. It does so by constructing a dual relation graph where the graph's vertices represent relationships in the original KG, enhancing the ability to understand how entities and relations interplay.

This approach is further refined through an iterative interaction mechanism between the original graph (primal graph) and its dual, enabling the model to embed richer relation information into entity representations. By integrating these enriched embeddings with Graph Convolutional Networks (GCNs) bolstered by highway gates, the model assimilates both structural neighborhood data and nuanced relation specifics.

Numerical Results

Strong numerical results substantiate the efficacy of RDGCN, with experiments conducted on three real-world, cross-lingual datasets from DBP15K - involving Chinese, English, Japanese, and French versions of DBpedia. The RDGCN model demonstrated a marked improvement over six recent alignment methods, particularly in terms of Hits@1 and Hits@10 metrics. For example, in the DBP15K $_{FR-EN}$ dataset, the RDGCN achieved an impressive Hits@1 score of 88.64%, outperforming existing methods and confirming its efficiency in handling cross-lingual KGs.

Implications and Future Directions

The implications of this research are notable both in theory and practice. From a theoretical standpoint, the paper offers a significant stride forward in knowledge representation, highlighting an innovative way to integrate relation information within the embeddings used for entity alignment. Practically, the model can be directly applied to a variety of AI applications that require the amalgamation of knowledge from different sources, such as recommendation systems and semantic web applications.

Moving forward, the integration of RDGCN with bootstrapping processes, which iteratively expand the training set using aligned data, could further enhance the capabilities of this model. Furthermore, exploring its adaptability to handle not just cross-lingual but also cross-domain KG alignment could broaden its scope of applicability.

In conclusion, the "Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs" manuscript elucidates a robust and nuanced approach to entity alignment by marrying dual graph convolutional networks with relational insights, setting a high watermark for both current methodologies and future innovations in the field.

PDF Markdown

Related Papers

Find Related Papers