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.
表中的内容
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.
关于作者
Heiko Paulheim is a computer scientist and a full professor for Data Science at the University of Mannheim. His group conducts various projects around knowledge graphs, yielding, among others, the public knowledge graphs Web Is ALOD, Ca Li Graph, and DBk Wik. Moreover, his group is concerned with using knowledge graphs in machine learning, which has lead to the development of the widespread RDF2vec method for knowledge graph embeddings. In the recent past, Heiko Paulheim also leads projects which are concerned with ethical, societal, and legal aspects of AI, including Kare Ko KI, which deals with the impact of price-setting AIs on antitrust legislation, and the Re New RS project on ethical news recommenders.
Petar Ristoski is an applied researcher at e Bay in San Jose, CA.
Jan Portisch is a Ph D student at the University of Mannheim in cooperation with SAP SE – Business Technology Platform – One Domain Model.
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