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WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data Analytics

Author

Listed:
  • Addi Ait-Mlouk

    (Department of Computing Science, Umeå University, 90187 Umeå, Sweden)

  • Xuan-Son Vu

    (Department of Computing Science, Umeå University, 90187 Umeå, Sweden)

  • Lili Jiang

    (Department of Computing Science, Umeå University, 90187 Umeå, Sweden)

Abstract

Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules.

Suggested Citation

  • Addi Ait-Mlouk & Xuan-Son Vu & Lili Jiang, 2020. "WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data Analytics," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2090-:d:449347
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    References listed on IDEAS

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    1. Michael Hahsler & Radoslaw Karpienko, 2017. "Visualizing association rules in hierarchical groups," Journal of Business Economics, Springer, vol. 87(3), pages 317-335, April.
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