Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns. (arXiv:2201.01996v1 [cs.LG])

The Resource Description Framework (RDF) is a framework for describing
metadata, such as attributes and relationships of resources on the Web. Machine
learning tasks for RDF graphs adopt three methods: (i) support vector machines
(SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relational
graph convolutional networks. In this paper, we propose a novel feature vector
(called a Skip vector) that represents some features of each resource in an RDF
graph by extracting various combinations of neighboring edges …

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