RDF2Vec: RDF graph embeddings and their applications

Ristoski, Petar ; Rosati, Jessica ; Di Noia, Tommaso ; De Leone, Renato ; Paulheim, Heiko

DOI: https://doi.org/10.3233/SW-180317
URL: https://content.iospress.com/articles/semantic-web...
Additional URL: http://www.semantic-web-journal.net/content/rdf2ve...
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: Semantic Web
Volume: 10
Issue number: 4
Page range: 721-752
Place of publication: Amsterdam
Publishing house: IOS Press
ISSN: 1570-0844 , 2210-4968
Publication language: English
Institution: School of Business Informatics and Mathematics > Web Data Mining (Paulheim 2018-)
Subject: 004 Computer science, internet
Abstract: Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs.We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs.We evaluate our approach on three different tasks: (i) standard machine learning tasks, (ii) entity and document modeling, and (iii) content-based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that pre-computed feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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