A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
This work provides a standardized evaluation framework for researchers in knowledge graph integration, though it is incremental as it builds on existing methods without introducing new paradigms.
The authors conducted a comprehensive benchmarking study of embedding-based entity alignment for knowledge graphs, surveying 23 approaches, proposing a new KG sampling algorithm to create benchmark datasets, and developing an open-source library with 12 representative methods for evaluation.
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.