Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.
@article{arxiv.2004.14843,
title = {Knowledge Graph Embeddings and Explainable AI},
author = {Federico Bianchi and Gaetano Rossiello and Luca Costabello and Matteo Palmonari and Pasquale Minervini},
journal= {arXiv preprint arXiv:2004.14843},
year = {2020}
}
Comments
Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Plamonari, Pasquale Minervini, Knowledge Graph Embeddings and Explainable AI. In: Ilaria Tiddi, Freddy Lecue, Pascal Hitzler (eds.), Knowledge Graphs for eXplainable AI -- Foundations, Applications and Challenges. Studies on the Semantic Web, IOS Press, Amsterdam, 2020