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Feature selection based on the complexity of structural patterns in RDF graphs

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Abstract

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. As a result, our proposed Skip vectors can represent the features of target resources in an RDF graph better than traditional methods and make conventional machine learning algorithms applicable to classification tasks in RDF data.

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Data availability

The datasets are available from the corresponding author on reasonable request.

Notes

  1. http://www.sw.cei.uec.ac.jp/frost/.

  2. https://www.wikidata.org/.

  3. http://ja.dbpedia.org/.

  4. https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/.

  5. https://github.com/tkipf/relational-gcn.

  6. The maximum value is chosen if many best parameters based on the validation set exist.

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP18K11547.

Funding

This work was supported by JSPS KAKENHI Grant Number JP18K11547.

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Authors and Affiliations

  1. Department of Computer and Network Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan

    Ken Kaneiwa & Yota Minami

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  1. Ken Kaneiwa
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  2. Yota Minami
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K. Kaneiwa designed the method and Y. Minami designed the method and conducted the experiments. All authors wrote and reviewed the main manuscript text.

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Correspondence to Ken Kaneiwa.

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Kaneiwa, K., Minami, Y. Feature selection based on the complexity of structural patterns in RDF graphs. Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00466-w

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