Open dataset discovery using context-enhanced similarity search

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00359555" target="_blank" >RIV/68407700:21240/22:00359555 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s10115-022-01751-z" target="_blank" >https://doi.org/10.1007/s10115-022-01751-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10115-022-01751-z" target="_blank" >10.1007/s10115-022-01751-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Open dataset discovery using context-enhanced similarity search

Popis výsledku v původním jazyce

Today, open data catalogs enable users to search for datasets with full-text queries in metadata records combined with simple faceted filtering. Using this combination, a user is able to discover a significant number of the datasets relevant to a user’s search intent. However, there still remain relevant datasets that are hard to find because of the enormous sparsity of their metadata (e.g., several keywords). As an alternative, in this paper, we propose an approach to dataset discovery based on similarity search over metadata descriptions enhanced by various semantic contexts. In general, the semantic contexts enrich the dataset metadata in a way that enables the identification of additional relevant datasets to a query that could not be retrieved using just the keyword or full-text search. In experimental evaluation we show that context-enhanced similarity retrieval methods increase the findability of relevant datasets, improving thus the retrieval recall that is critical in dataset discovery scenarios. As a part of the evaluation, we created a catalog-like user interface for dataset discovery and recorded streams of user actions that served us to create the ground truth. For the sake of reproducibility, we published the entire evaluation testbed.

Název v anglickém jazyce

Open dataset discovery using context-enhanced similarity search

Popis výsledku anglicky

Today, open data catalogs enable users to search for datasets with full-text queries in metadata records combined with simple faceted filtering. Using this combination, a user is able to discover a significant number of the datasets relevant to a user’s search intent. However, there still remain relevant datasets that are hard to find because of the enormous sparsity of their metadata (e.g., several keywords). As an alternative, in this paper, we propose an approach to dataset discovery based on similarity search over metadata descriptions enhanced by various semantic contexts. In general, the semantic contexts enrich the dataset metadata in a way that enables the identification of additional relevant datasets to a query that could not be retrieved using just the keyword or full-text search. In experimental evaluation we show that context-enhanced similarity retrieval methods increase the findability of relevant datasets, improving thus the retrieval recall that is critical in dataset discovery scenarios. As a part of the evaluation, we created a catalog-like user interface for dataset discovery and recorded streams of user actions that served us to create the ground truth. For the sake of reproducibility, we published the entire evaluation testbed.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

0219-3116

Svazek periodika

64

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

27

Strana od-do

3265-3291

Kód UT WoS článku

000849677000001

EID výsledku v databázi Scopus

2-s2.0-85137453544