Modular framework for similarity-based dataset discovery using external knowledge

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Modular framework for similarity-based dataset discovery using external knowledge

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modular framework for similarity-based dataset discovery using external knowledge

Popis výsledku anglicky

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.

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

Data Technologies and Applications

ISSN

2514-9288

e-ISSN

2514-9318

Svazek periodika

56

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

30

Strana od-do

506-535

Kód UT WoS článku

000759634600001

EID výsledku v databázi Scopus

2-s2.0-85125073753