Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Federico Bianchi
- sl:arxiv_num : 2004.14843
- sl:arxiv_published : 2020-04-30T14:55:09Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Knowledge Graph Embeddings and Explainable AI@en
- sl:arxiv_updated : 2020-04-30T14:55:09Z
- sl:bookmarkOf : https://arxiv.org/abs/2004.14843
- sl:creationDate : 2020-05-04
- sl:creationTime : 2020-05-04T13:29:14Z
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