ALBERT, Adam, Marie DUŽÍ, Marek MENŠÍK, Miroslav PAJR and Vojtěch PATSCHKA. Search for appropriate textual information sources. In Tropmann-Frick, Marina; Thalheim Bernhard; Jaakkola, Hannu; Kiyoki, Yasushi; Yoshida, Naufomi. Frontiers in Artificial Intelligence and Applications: Information Modelling and Knowledge Bases XXXII. 333rd ed. Amsterodam: IOS Press BV. p. 227-246. ISBN 978-1-64368-140-5. doi:10.3233/FAIA200832. 2020.
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Basic information
Original name Search for appropriate textual information sources
Authors ALBERT, Adam (203 Czech Republic), Marie DUŽÍ (203 Czech Republic), Marek MENŠÍK (203 Czech Republic), Miroslav PAJR (203 Czech Republic, guarantor, belonging to the institution) and Vojtěch PATSCHKA (203 Czech Republic).
Edition 333. vyd. Amsterodam, Frontiers in Artificial Intelligence and Applications: Information Modelling and Knowledge Bases XXXII, p. 227-246, 20 pp. 2020.
Publisher IOS Press BV
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW Domovská stránka s informacemi o výsledku, možnost stažení plného textu (open access)
RIV identification code RIV/47813059:19240/20:A0000883
Organization unit Faculty of Philosophy and Science in Opava
ISBN 978-1-64368-140-5
ISSN 0922-6389
Doi http://dx.doi.org/10.3233/FAIA200832
Keywords in English Association rules; Atomic concept; Explication; Information source recommendation; Machine learning; Molecular concept; Natural language processing; TIL; Transparent Intensional Logic
Tags ÚI
Tags International impact, Reviewed
Links LQ1602, research and development project.
Changed by Changed by: Mgr. Kamil Matula, Ph.D., učo 7389. Changed: 21/12/2021 09:53.
Abstract
In this paper, we deal with the support in the search for appropriate textual sources. Users ask for an atomic concept that is explicated using machine learning methods applied to different textual sources. Next, we deal with the so-obtained explications to provide even more useful information. To this end, we apply the method of computing association rules. The method is one of the data-mining methods used for information retrieval. Our background theory is the system of Transparent Intensional Logic (TIL); all the concepts are formalised as TIL constructions.
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