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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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樓主: 螺絲刀
31#
發(fā)表于 2025-3-26 23:18:55 | 只看該作者
https://doi.org/10.1007/978-3-031-30387-6Data mining; knowledge representation in AI; Knowledge Graph Embeddings; dynamic knowledge graphs; ontol
32#
發(fā)表于 2025-3-27 04:15:50 | 只看該作者
33#
發(fā)表于 2025-3-27 09:00:50 | 只看該作者
34#
發(fā)表于 2025-3-27 11:19:22 | 只看該作者
2691-2023 lications.Discusses which variants of RDF2vec exist and when.This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reus
35#
發(fā)表于 2025-3-27 16:30:19 | 只看該作者
Book 2023red vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode..
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發(fā)表于 2025-3-27 20:13:18 | 只看該作者
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發(fā)表于 2025-3-28 01:34:52 | 只看該作者
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發(fā)表于 2025-3-28 05:15:30 | 只看該作者
From Word Embeddings to Knowledge Graph Embeddings,approach can be adapted to knowledge graphs by performing random graph walks, yielding the basic version of RDF2vec. We explain the CBOW and SkipGram variants of basic RDF2vec, revisiting the node classification tasks used in Chap.?..
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