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Explainable Similarity of Datasets Using Knowledge Graph

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11807)


There is a large quantity of datasets available as Open Data on the Web. However, it is challenging for users to find datasets relevant to their needs, even though the datasets are registered in catalogs such as the European Data Portal. This is because the available metadata such as keywords or textual description is not descriptive enough. At the same time, datasets exist in various types of contexts not expressed in the metadata. These may include information about the dataset publisher, the legislation related to dataset publication, language and cultural specifics, etc. In this paper we introduce a similarity model for matching datasets. The model assumes an ontology/knowledge graph, such as, that serves as a graph-based context to which individual datasets are mapped based on their metadata. A similarity of the datasets is then computed as an aggregation over paths among nodes in the graph. The proposed similarity aims at addressing the problem of explainability of similarity, i.e., providing the user a structured explanation of the match which, in a broader sense, is nowadays a hot topic in the field of artificial intelligence.


  • Similarity
  • Datasets
  • Search
  • Graph

This research has been supported by Czech Science Foundation (GAČR) project Nr. 19-01641S.

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  • DOI: 10.1007/978-3-030-32047-8_10
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Correspondence to Tomáš Skopal .

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Škoda, P., Klímek, J., Nečaský, M., Skopal, T. (2019). Explainable Similarity of Datasets Using Knowledge Graph. In: Amato, G., Gennaro, C., Oria, V., Radovanović , M. (eds) Similarity Search and Applications. SISAP 2019. Lecture Notes in Computer Science(), vol 11807. Springer, Cham.

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