Open dataset discovery using context-enhanced similarity search

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Open dataset discovery using context-enhanced similarity search

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

0219-3116

Volume of the periodical

64

Issue of the periodical within the volume

12

Country of publishing house

DE - GERMANY

Number of pages

27

Pages from-to

3265-3291

UT code for WoS article

000849677000001

EID of the result in the Scopus database

2-s2.0-85137453544