Titlebook: Embedding Knowledge Graphs with RDF2vec; Heiko Paulheim,Petar Ristoski,Jan Portisch Book 2023 The Editor(s) (if applicable) and The Author

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Talking About Talking Microbes,ification, which we have considered so far). In this chapter, we give a very brief overview of the main embedding techniques for link prediction and flesh out the main differences between the well-known link prediction technique TransE and RDF2vec. Moreover, we show how RDF2vec can be used for link

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https://doi.org/10.1007/978-3-030-16190-3re are the handling of literal values (which are currently not used by RDF2vec), the handling of dynamic knowledge graphs, and the generation of are explanations for systems using RDF2vec (which are currently black box models).

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Heiko Paulheim,Petar Ristoski,Jan PortischExplains what are knowledge graph embeddings are and how they can be computed.Demonstrates how RDF2vec is used as a building block in AI applications.Discusses which variants of RDF2vec exist and when

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Synthesis Lectures on Data, Semantics, and Knowledgehttp://image.papertrans.cn/e/image/307983.jpg

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Poornima Singh,Mohit Sharma,Rashmi Rawater introduces a few datasets and three common benchmarks for embedding methods—i.e., SW4ML, GEval, and DLCC—and shows how to use them for comparing different variants of RDF2vec. The novel DLCC benchmark allows us to take a closer look at what RDF2vec vectors actually represent, and to analyze what proximity in the vector space means for them.