A benchmarking study of embedding-based entity alignment for knowledge graphs
By Zequn Sun, Qingheng Zhang, Wei Hu, Chengming Wang, Muhao Chen, Farahnaz Akrami, Chengkai Li. Proceedings of the VLDB Endowment, 2020
313 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/65baa67a7cdb3b4b948d126ac5b41ca9c98b1f3b
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In Artificial Intelligence
In Management Science and Operations Research
Citations in the corpus
- A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “For example, OpenKE [49] provides multiple knowledge representation models for knowledge graph completion, while OpenEA [50] leverages similar methods for merging structures in knowledge graphs.”
- A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, background): “• MKG [181], [241] consists of two subsets, i.”