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Open dataset discovery using context-enhanced similarity search

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Abstract

Today, open data catalogs enable users to search for datasets with full-text queries in metadata records combined with simple faceted filtering. Using this combination, a user is able to discover a significant number of the datasets relevant to a user’s search intent. However, there still remain relevant datasets that are hard to find because of the enormous sparsity of their metadata (e.g., several keywords). As an alternative, in this paper, we propose an approach to dataset discovery based on similarity search over metadata descriptions enhanced by various semantic contexts. In general, the semantic contexts enrich the dataset metadata in a way that enables the identification of additional relevant datasets to a query that could not be retrieved using just the keyword or full-text search. In experimental evaluation we show that context-enhanced similarity retrieval methods increase the findability of relevant datasets, improving thus the retrieval recall that is critical in dataset discovery scenarios. As a part of the evaluation, we created a catalog-like user interface for dataset discovery and recorded streams of user actions that served us to create the ground truth. For the sake of reproducibility, we published the entire evaluation testbed.

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Notes

  1. https://data.europa.eu.

  2. https://data.gov.cz.

  3. https://www.data.gov/.

  4. Note that the data-engineering terminology could be a bit misleading. A database is generally a collection of database objects (or documents). In our case, the database object (document) is a particular dataset (e.g., a CSV file). Hence, in dataset discovery we search a database of datasets.

  5. https://data.europa.eu/en.

  6. https://gitlab.com/european-data-portal/metrics/edp-metrics-dataset-similarities/-/blob/master/src/main/java/io/piveau/metrics/similarities/SimilarityVerticle.java.

  7. https://github.com/mff-uk/simpipes-components/blob/main/processors/compute-similarity/basic/linda/tlsh.py.

  8. https://data.gov.cz.

  9. The numbers can be computed using query1.sparql published with the dataset [50].

  10. The numbers can be computed using query2.sparql published with the dataset [50].

  11. The numbers can be computed using query3.sparql published with the dataset [50].

  12. https://measuringu.com/sus/.

  13. https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html.

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Acknowledgements

This work was supported by the Czech Science Foundation (GAČR), grant number 19-01641S.

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Department of Software Engineering, Charles University, Malostranské náměstí 25, Prague, 118 00, Czechia

    David Bernhauer, Martin Nečaský, Petr Škoda, Jakub Klímek & Tomáš Skopal

  2. Faculty of Information Technology, Department of Software Engineering, Czech Technical University in Prague, Thákurova 9, Prague, 160 00, Czechia

    David Bernhauer

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  1. David Bernhauer
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Bernhauer, D., Nečaský, M., Škoda, P. et al. Open dataset discovery using context-enhanced similarity search. Knowl Inf Syst 64, 3265–3291 (2022). https://doi.org/10.1007/s10115-022-01751-z

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