Explainable Similarity of Datasets using Knowledge Graph

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Explainable Similarity of Datasets using Knowledge Graph

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Explainable Similarity of Datasets using Knowledge Graph

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA19-01641S" target="_blank" >GA19-01641S: Kontextové podobnostní vyhledávání v otevřených datech</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-030-32046-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

8

Strana od-do

103-110

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Newark NJ, USA

Datum konání akce

2. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—