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The Resource Description Framework (RDF) is a framework for describingmetadata, such as attributes and relationships of resources on the Web. Machinelearning tasks for RDF graphs adopt three methods: (i) support vector machines(SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relationalgraph convolutional networks. In this paper, we propose a novel feature vector(called a Skip vector) that represents some features of each resource in an RDFgraph by extracting various combinations of neighboring edges and nodes. Inorder to make the Skip vector low-dimensional, we select important features forclassification tasks based on the information gain ratio of each feature. Theclassification tasks can be performed by applying the low-dimensional Skipvector of each resource to conventional machine learning algorithms, such asSVMs, the k-nearest neighbors method, neural networks, random forests, andAdaBoost. In our evaluation experiments with RDF data, such as Wikidata,DBpedia, and YAGO, we compare our method with RDF graph kernels in an SVM. Wealso compare our method with the two approaches: RDF graph embeddings such asRDF2vec and relational graph convolutional networks on the AIFB, MUTAG, BGS,and AM benchmarks.

Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns