[3G1-ES-1] Knowledge engineering (1)

[3G1-ES-1-04] Entity Alignment for Heterogeneous Knowledge Graphs using Summary and Attribute Embeddings

〇Rumana Ferdous Munne¹, Ryutaro Ichise¹ (1. National Institute of Informatics)

キーワード：knowledge graphs , Entity alignment, Embedding models

Knowledge Graph (KG) is a well known way of representing facts about the
real world in the form of entities, where nodes and edges represent the entities
and their respective relations. However, many KGs have been constructed inde-
pendently for different purpose. Therefore, very limited number of the entities
stored in different KGs are aligned. This paper presents an embedding-based en-
tity alignment method. We propose a joint method of summary and attribute
embeddings for entity alignment task. Our model learns the representations
of entities by using relational triples, attribute triples and description as well.
When entities have less number of attributes or when the relational structure
couldn’t capture the meaningful representation of the entities, entity summary
embedding can be useful. We perform experiments on real-world datasets and
the results indicate that the proposed approach significantly outperformed the
state-of-the- art models for entity alignment.