Название: Embedding Knowledge Graphs with RDF2vecАвтор: Heiko Paulheim, Petar Ristoski, Jan Portisch
Издательство: Springer
Год: 2023
Страниц: 165
Язык: английский
Формат: pdf (true), epub
Размер: 19.6 MB
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. Knowledge graph embedding is a technique which projects entities and relations in a knowledge graph into a continuous vector space. Many other components of AI systems, especially machine learning components, can work with those continuous representations better than operating on the graph itself, and often yield superior result quality compared to those trying to extract non-continuous features from a graph.
Название: Ground-Based Radar in Structural Design, Optimization, and Health Monitoring of Stationary and Rotating Structures
Название: Security-First Compliance for Small Businesses
Название: Machine Learning Interviews (Second Early Release)
