ReliK: A Reliability Measure for Knowledge Graph Embeddings
Maximilian K. Egger, Wenyue Ma, Davide Mottin, Panagiotis Karras, Ilaria Bordino, Francesco Gullo, Aris Anagnostopoulos · 2024
Can we assess a priori how well a knowledge graph embedding will perform on a specific downstream task and in a specific part of the knowledge graph? Knowledge graph embeddings (KGEs) represent entities (e.g., ''da Vinci,'' ''Mona Lisa'') and relationships (e.g., ''painted'') of a knowledge graph (KG) as vectors. KGEs are generated by optimizing an embedding score, which assesses whether a triple (e.g., ''da Vinci,'' "painted,'' ''Mona Lisa'') exists in the graph. KGEs have been proven effective in a variety of web-related downstream tasks, including, for instance, predicting relationship(s) among entities. However, the problem of anticipating the performance of a given KGE in a certain downstream task and locally to a specific individual triple, has not been tackled so far.