Modular framework for similarity-based dataset discovery using external knowledge

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00356040" target="_blank" >RIV/68407700:21240/22:00356040 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1108/DTA-09-2021-0261" target="_blank" >https://doi.org/10.1108/DTA-09-2021-0261</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1108/DTA-09-2021-0261" target="_blank" >10.1108/DTA-09-2021-0261</a>

Result language

angličtina

Original language name

Modular framework for similarity-based dataset discovery using external knowledge

Original language description

Purpose Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth. Design/methodology/approach In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery. Findings The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework. Originality/value To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

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

Data Technologies and Applications

ISSN

2514-9288

e-ISSN

2514-9318

Volume of the periodical

56

Issue of the periodical within the volume

4

Country of publishing house

GB - UNITED KINGDOM

Number of pages

30

Pages from-to

506-535

UT code for WoS article

000759634600001

EID of the result in the Scopus database

2-s2.0-85125073753