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.
Bianchi, F., Rossiello, G., Costabello, L., Palmonari, M., Minervini, P. (2020). Knowledge Graph Embeddings and Explainable AI. In I. Tiddi, F. Lécué, P. Hitzler (a cura di), Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges (pp. 49-72). NEUE PROMENADE 6, 10178 BERLIN, GERMANY : IOS Press [10.3233/SSW200011].
Knowledge Graph Embeddings and Explainable AI
Bianchi Federico;Palmonari Matteo;
2020
Abstract
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.
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