Knowledge graph embedding methods for entity alignment: experimental review

• Published in: Data mining and knowledge discovery Vol. 37; no. 5; pp. 2070 - 2137
• Main Authors: Fanourakis, Nikolaos, Efthymiou, Vasilis, Kotzinos, Dimitris, Christophides, Vassilis
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2023; Springer Nature B.V; Springer
• Subjects: Alignment; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Effectiveness; Efficiency; Embedding; Graph theory; Graphs; Information Storage and Retrieval; Knowledge representation; Names; Performance measurement; Physics; Statistical methods; Statistics for Engineering; Entity alignment; Knowledge graph embeddings; Knowledge graphs
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-023-00941-9

In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A frequent task when integrating knowledge from different KGs is to find which subgraphs refer to the same real-world entity, a task largely known as the Entity Alignment. Recently, embedding methods have been used for entity alignment tasks, that learn a vector-space representation of entities which preserves their similarity in the original KGs. A wide variety of supervised, unsupervised, and semi-supervised methods have been proposed that exploit both factual (attribute based) and structural information (relation based) of entities in the KGs. Still, a quantitative assessment of their strengths and weaknesses in real-world KGs according to different performance metrics and KG characteristics is missing from the literature. In this work, we conduct the first meta-level analysis of popular embedding methods for entity alignment, based on a statistically sound methodology. Our analysis reveals statistically significant correlations of different embedding methods with various meta-features extracted by KGs and rank them in a statistically significant way according to their effectiveness across all real-world KGs of our testbed. Finally, we study interesting trade-offs in terms of methods’ effectiveness and efficiency.