Computer Science > Computation and Language
[Submitted on 10 Mar 2020 (v1), last revised 20 Jul 2020 (this version, v2)]
Title:A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
View PDFAbstract:Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. In this paper, we conduct a comprehensive experimental study of this emerging field. We survey 23 recent embedding-based entity alignment approaches and categorize them based on their techniques and characteristics. We also propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation. We develop an open-source library including 12 representative embedding-based entity alignment approaches, and extensively evaluate these approaches, to understand their strengths and limitations. Additionally, for several directions that have not been explored in current approaches, we perform exploratory experiments and report our preliminary findings for future studies. The benchmark datasets, open-source library and experimental results are all accessible online and will be duly maintained.
Submission history
From: Wei Hu [view email][v1] Tue, 10 Mar 2020 05:32:06 UTC (514 KB)
[v2] Mon, 20 Jul 2020 00:47:26 UTC (855 KB)
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