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Visualizer of Dataset Similarity Using Knowledge Graph

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10420919" target="_blank" >RIV/00216208:11320/20:10420919 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-030-60936-8_29" target="_blank" >https://doi.org/10.1007/978-3-030-60936-8_29</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-60936-8_29" target="_blank" >10.1007/978-3-030-60936-8_29</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Visualizer of Dataset Similarity Using Knowledge Graph

  • Original language description

    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 &quot;black-box&quot; 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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA19-01641S" target="_blank" >GA19-01641S: Contextual Similarity Search in Open Data</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Similarity Search and Applications. SISAP 2020.

  • ISBN

    978-3-030-60936-8

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    371-378

  • Publisher name

    Springer

  • Place of publication

    Cham, Germany

  • Event location

    Copenhagen, Denmark

  • Event date

    Sep 30, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article