Improvement of Searching for Appropriate Textual Information Sources Using Association Rules and FCA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249576" target="_blank" >RIV/61989100:27240/21:10249576 - isvavai.cz</a>

Výsledek na webu

<a href="https://ebooks.iospress.nl/doi/10.3233/FAIA210487" target="_blank" >https://ebooks.iospress.nl/doi/10.3233/FAIA210487</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA210487" target="_blank" >10.3233/FAIA210487</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improvement of Searching for Appropriate Textual Information Sources Using Association Rules and FCA

Popis výsledku v původním jazyce

This paper deals with an optimization of methods for recommending relevant text sources. We summarize methods that are based on a theory of Association Rules and Formal Conceptual Analysis which are computationally demanding. Therefore we are applying the &apos;Iceberg Concepts&apos;, which significantly prune output data space and thus accelerate the whole process of the calculation. Association Rules and the Relevant Ordering, which is an FCA-based method, are applied on data obtained from explications of an atomic concept. Explications are procured from natural language sentences formalized into TIL constructions and processed by a machine learning algorithm. TIL constructions are utilized only as a specification language and they are described in numerous publications, so we do not deal with TIL in this paper. (C) 2021 The authors and IOS Press.

Název v anglickém jazyce

Improvement of Searching for Appropriate Textual Information Sources Using Association Rules and FCA

Popis výsledku anglicky

This paper deals with an optimization of methods for recommending relevant text sources. We summarize methods that are based on a theory of Association Rules and Formal Conceptual Analysis which are computationally demanding. Therefore we are applying the &apos;Iceberg Concepts&apos;, which significantly prune output data space and thus accelerate the whole process of the calculation. Association Rules and the Relevant Ordering, which is an FCA-based method, are applied on data obtained from explications of an atomic concept. Explications are procured from natural language sentences formalized into TIL constructions and processed by a machine learning algorithm. TIL constructions are utilized only as a specification language and they are described in numerous publications, so we do not deal with TIL in this paper. (C) 2021 The authors and IOS Press.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2021

Kód důvěrnosti údajů

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

Název periodika

Frontiers in Artificial Intelligence and Applications. Volume 343

ISSN

0922-6389

e-ISSN

—

Svazek periodika

343

Číslo periodika v rámci svazku

343

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

204-214

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85123752950