Abstract
Many institutions choose to make their datasets available as Open Data. Open Data datasets are described by publisher-provided metadata and are registered in catalogs such as the European Data Portal. In spite of that, findability still remain a major issue. One of the main reasons is that metadata is captured in different contexts and with different background knowledge, so that keyword-based search provided by the catalogs is insufficient. A solution is to use an enriched querying that employs a dataset similarity model built on a shared context represented by a knowledge graph. However, the “black-box” dataset similarity may not fit well the user needs. If an explainable similarity model is used, then the issue can be tackled by providing users with a visualisation of the dataset similarity. This paper introduces a web-based tool for dataset similarity visualisation called ODIN (Open Dataset INspector). ODIN visualises knowledge graph-based dataset similarity, offering thus an explanation to the user. To understand the similarity, users can discover additional datasets that match their needs or reformulate the query to better reflect the knowledge graph. Last but not least, the user can analyze and/or design the similarity model itself.
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e.g., European Data Portal, https://www.europeandataportal.eu.
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References
Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (eds.): Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28954-6
Škoda, P., Klímek, J., Nečaský, M., Skopal, T.: Explainable similarity of datasets using knowledge graph. In: Amato, G., Gennaro, C., Oria, V., Radovanović, M. (eds.) SISAP 2019. LNCS, vol. 11807, pp. 103–110. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32047-8_10
Skopal, T., Bustos, B.: On nonmetric similarity search problems in complexdomains. ACM Comput. Surv. 43(4) (2011). https://doi.org/10.1145/1978802.1978813
Straka, M., Straková, J.: Tokenizing, POS tagging, lemmatizing and parsing UD 2.0 with udpipe. In: CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pp. 88–99. Association for Computational Linguistics (2017)
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach, Advances in Database Systems, vol. 32. Springer, Boston (2006). https://doi.org/10.1007/0-387-29151-2
Acknowledgments
This research has been supported by Czech Science Foundation (GAČR) project Nr. 19-01641S.
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Škoda, P., Matějík, J., Skopal, T. (2020). Visualizer of Dataset Similarity Using Knowledge Graph. In: Satoh, S., et al. Similarity Search and Applications. SISAP 2020. Lecture Notes in Computer Science(), vol 12440. Springer, Cham. https://doi.org/10.1007/978-3-030-60936-8_29
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DOI: https://doi.org/10.1007/978-3-030-60936-8_29
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