Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns
This work addresses the challenge of efficient feature extraction for RDF data in machine learning, offering a domain-specific solution that is incremental in nature.
The paper tackles the problem of representing resources in RDF graphs for machine learning tasks by proposing Skip vectors, which extract neighboring features and reduce dimensionality based on information gain ratio, achieving competitive performance on benchmarks like AIFB and MUTAG compared to existing methods such as RDF graph kernels and embeddings.
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 and nodes. In order to make the Skip vector low-dimensional, we select important features for classification tasks based on the information gain ratio of each feature. The classification tasks can be performed by applying the low-dimensional Skip vector of each resource to conventional machine learning algorithms, such as SVMs, the k-nearest neighbors method, neural networks, random forests, and AdaBoost. In our evaluation experiments with RDF data, such as Wikidata, DBpedia, and YAGO, we compare our method with RDF graph kernels in an SVM. We also compare our method with the two approaches: RDF graph embeddings such as RDF2vec and relational graph convolutional networks on the AIFB, MUTAG, BGS, and AM benchmarks.