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Embedding Knowledge Graphs with RDF2vec [electronic resource] / by Heiko Paulheim, Petar Ristoski, Jan Portisch.

By: Paulheim, Heiko [author.].
Contributor(s): Ristoski, Petar [author.] | Portisch, Jan [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Data, Semantics, and Knowledge: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: IX, 158 p. 43 illus., 27 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031303876.Subject(s): Artificial intelligence | Data mining | Expert systems (Computer science) | Artificial Intelligence | Data Mining and Knowledge Discovery | Knowledge Based SystemsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- From Word Embeddings to Knowledge Graph Embeddings -- RDF2vec Variants and Representations -- Tweaking RDF2vec -- RDF2vec at Scale -- Example Applications beyond Node Classification -- Link Prediction in Knowledge Graphs (and its Relation to RDF2vec) -- Future Directions for RDF2vec.
In: Springer Nature eBookSummary: 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 reusing pre-trained knowledge graph embeddings to training tailored 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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Introduction -- From Word Embeddings to Knowledge Graph Embeddings -- RDF2vec Variants and Representations -- Tweaking RDF2vec -- RDF2vec at Scale -- Example Applications beyond Node Classification -- Link Prediction in Knowledge Graphs (and its Relation to RDF2vec) -- Future Directions for RDF2vec.

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 reusing pre-trained knowledge graph embeddings to training tailored 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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