Seeking Relevant Information Sources

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F19%3AA0000685" target="_blank" >RIV/47813059:19240/19:A0000685 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9119332" target="_blank" >https://ieeexplore.ieee.org/document/9119332</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/Informatics47936.2019.9119332" target="_blank" >10.1109/Informatics47936.2019.9119332</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Seeking Relevant Information Sources

Popis výsledku v původním jazyce

In this paper we deal with the problem of seeking relevant information sources selected from scientific or other electronic publications. In the era of information surfeit, it is getting more and more difficult to extract relevant and reliable sources of information from the huge number of e-sources. The starting point is user's query for a given concept or topic. Our algorithm applies machine learning methods in order to propose hypothetic explications of the sought terms based on pieces of information extracted from the potentially relevant e-sources. Hypotheses, formalized in the TIL-Script language, are incrementally built by applying heuristic functions. The user thus obtains as closed approximations of the meaning of the sought terms as possible that at the same time provide fine-grained keyword definitions. As a result, it should be much easier to decide which of the e-sources are relevant for user's interest.

Název v anglickém jazyce

Seeking Relevant Information Sources

Popis výsledku anglicky

In this paper we deal with the problem of seeking relevant information sources selected from scientific or other electronic publications. In the era of information surfeit, it is getting more and more difficult to extract relevant and reliable sources of information from the huge number of e-sources. The starting point is user's query for a given concept or topic. Our algorithm applies machine learning methods in order to propose hypothetic explications of the sought terms based on pieces of information extracted from the potentially relevant e-sources. Hypotheses, formalized in the TIL-Script language, are incrementally built by applying heuristic functions. The user thus obtains as closed approximations of the meaning of the sought terms as possible that at the same time provide fine-grained keyword definitions. As a result, it should be much easier to decide which of the e-sources are relevant for user's interest.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 IEEE 15th International Scientific Conference on Informatics

ISBN

9781728131818

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

255-260

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Montreal, Canada

Místo konání akce

Poprad, Slovakia

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—