Refining Concepts by Machine Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242944" target="_blank" >RIV/61989100:27240/19:10242944 - isvavai.cz</a>
Alternative codes found
RIV/47813059:19240/19:A0000487
Result on the web
<a href="https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3242/2663" target="_blank" >https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3242/2663</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13053/CyS-23-3-3242" target="_blank" >10.13053/CyS-23-3-3242</a>

Result language
angličtina
Original language name
Refining Concepts by Machine Learning
Original language description
In this paper we deal with machine learning methods and algorithms applied in learning simple concepts by their refining or explication. The method of refining a simple concept of an object O consists in discovering a molecular concept that defines the same or a very similar object to the object O. Typically, such a molecular concept is a professional definition of the object, for instance a biological definition according to taxonomy, or legal definition of roles, acts, etc. Our background theory is Transparent Intensional Logic (TIL). In TIL concepts are explicated as abstract procedures encoded by natural language terms. These procedures are defined as six kinds of TIL constructions. First, we briefly introduce the method of learning with a supervisor that is applied in our case. Then we describe the algorithm 'Framework' together with heuristic methods applied by it. The heuristics is based on a plausible supply of positive and negative (near-miss) examples by which learner's hypotheses are refined and adjusted. Given a positive example, the learner refines the hypothesis learnt so far, while a near-miss example triggers specialization. Our heuristic methods deal with the way refinement is applied, which includes also its special cases generalization and specialization.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - 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
<a href="/en/project/GA18-23891S" target="_blank" >GA18-23891S: Hyperintensional Reasoning over Natural Language Texts</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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