Knowledge graph embedding methods for entity alignment: experimental review

Nikolaos Fanourakis (1, 2) , Vasilis Efthymiou (1) , Dimitris Kotzinos (3) , Vassilis Christophides (3)

1 ICS-FORTH - Institute of Computer Science [FORTH, Heraklion]
2 CSD-UOC - Computer Science Department [Crete]
3 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes

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.

Mots clés: Knowledge graph embeddings; Entity alignment; Knowledge graphs

Domaines: Informatique [cs]

Nikolaos Fanourakis, Vasilis Efthymiou, Dimitris Kotzinos, Vassilis Christophides. Knowledge graph embedding methods for entity alignment: experimental review. Data Mining and Knowledge Discovery, 2023, 37 (5), pp.2070-2137. ⟨10.1007/S10618-023-00941-9⟩. ⟨hal-04319674⟩