Explainable Similarity of Datasets using Knowledge Graph

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10398464" target="_blank" >RIV/00216208:11320/19:10398464 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-030-32047-8_10" target="_blank" >https://doi.org/10.1007/978-3-030-32047-8_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-32047-8_10" target="_blank" >10.1007/978-3-030-32047-8_10</a>

Result language

angličtina

Original language name

Explainable Similarity of Datasets using Knowledge Graph

Original language description

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 Wikidata.org, 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

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)

Publication year

2019

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-030-32046-1

ISSN

0302-9743

e-ISSN

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Number of pages

8

Pages from-to

103-110

Publisher name

Springer International Publishing

Place of publication

Cham

Event location

Newark NJ, USA

Event date

Oct 2, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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