Enhancing Entity Alignment Between Wikidata and ArtGraph using LLMs

Knowledge graphs (KGs) are used in a wide variety of applications, including within the cultural heritage domain. An important prerequisite of such applications is the quality and completeness of the data. Using a single KG might not be enough to fulfill this requirement. The absence of connections between KGs complicates taking advantage of the complementary data they can provide. This paper focuses on the Wikidata and A 𝑟𝑡G 𝑟𝑎𝑝ℎ KGs, which exhibit gaps in content that can be filled by enriching one with data from the other. Entity alignment can help to combine data from KGs by connecting entities that refer to the same real-world entities. However, entity alignment in art-domain knowledge graphs remains under-explored. In the pursuit of entity alignment between A 𝑟𝑡G 𝑟𝑎𝑝ℎ and Wikidata, a hybrid approach is proposed. The first part, which we call WES (Wikidata Entity Search), utilizes traditional Wikidata SPARQL queries and is followed by a supplementary sequence-to-sequence large language model (LLM) pipeline that we denote as pArtLink. The combined approach successfully aligned artworks and artists, with WES identifying entities for 14,982 artworks and 2,029 artists, and pArtLink further aligning 76 additional artists, thus enhancing the alignment process beyond WES’ capabilities.