Evaluation Framework for Search Methods Focused on Dataset Findability in Open Data Catalogs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00342949" target="_blank" >RIV/68407700:21240/20:00342949 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/20:10420920

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3428757.3429973" target="_blank" >http://dx.doi.org/10.1145/3428757.3429973</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3428757.3429973" target="_blank" >10.1145/3428757.3429973</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation Framework for Search Methods Focused on Dataset Findability in Open Data Catalogs

Popis výsledku v původním jazyce

Many institutions publish datasets as Open Data in catalogs, however, their retrieval remains problematic issue due to the absence of dataset search benchmarking. We propose a framework for evaluating findability of datasets, regardless of retrieval models used. As task-agnostic labeling of datasets by ground truth turns out to be infeasible in the general domain of open data datasets, the proposed framework is based on evaluation of entire retrieval scenarios that mimic complex retrieval tasks. In addition to the framework we present a proof of concept specification and evaluation on several similarity-based retrieval models and several dataset discovery scenarios within a catalog, using our experimental evaluation tool. Instead of traditional matching of query with metadata of all the datasets, in similarity-based retrieval the query is formulated using a set of datasets (query by example) and the most similar datasets to the query set are retrieved from the catalog as a result.

Název v anglickém jazyce

Evaluation Framework for Search Methods Focused on Dataset Findability in Open Data Catalogs

Popis výsledku anglicky

Many institutions publish datasets as Open Data in catalogs, however, their retrieval remains problematic issue due to the absence of dataset search benchmarking. We propose a framework for evaluating findability of datasets, regardless of retrieval models used. As task-agnostic labeling of datasets by ground truth turns out to be infeasible in the general domain of open data datasets, the proposed framework is based on evaluation of entire retrieval scenarios that mimic complex retrieval tasks. In addition to the framework we present a proof of concept specification and evaluation on several similarity-based retrieval models and several dataset discovery scenarios within a catalog, using our experimental evaluation tool. Instead of traditional matching of query with metadata of all the datasets, in similarity-based retrieval the query is formulated using a set of datasets (query by example) and the most similar datasets to the query set are retrieved from the catalog as a result.

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services

ISBN

978-1-4503-8924-2

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

200-209

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Chiang Mai (online)

Datum konání akce

30. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—