Entity Linking and Lexico-Semantic Patterns for Ontology Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00341966" target="_blank" >RIV/68407700:21230/20:00341966 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-49461-2_9" target="_blank" >https://doi.org/10.1007/978-3-030-49461-2_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-49461-2_9" target="_blank" >10.1007/978-3-030-49461-2_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Entity Linking and Lexico-Semantic Patterns for Ontology Learning

Popis výsledku v původním jazyce

Ontology learning from a text written in natural language is a well-studied domain. However, the applicability of techniques for ontology learning from natural language texts is strongly dependent on the characteristics of the text corpus and the language used. In this paper, we present our work so far in entity linking and enhancing the ontology with extracted relations between concepts. We discuss the benefits of adequately designed lexico-semantic patterns in ontology learning. We propose a preliminary set of lexico-semantic patterns designed for the Czech language to learn new relations between concepts in the related domain ontology in a semi-supervised approach. We utilize data from the urban planning and development domain to evaluate the introduced technique. As a partial prototypical implementation of the stack, we present Annotace, a text annotation service that provides links between the ontology model and the textual documents in Czech.

Název v anglickém jazyce

Entity Linking and Lexico-Semantic Patterns for Ontology Learning

Popis výsledku anglicky

Ontology learning from a text written in natural language is a well-studied domain. However, the applicability of techniques for ontology learning from natural language texts is strongly dependent on the characteristics of the text corpus and the language used. In this paper, we present our work so far in entity linking and enhancing the ontology with extracted relations between concepts. We discuss the benefits of adequately designed lexico-semantic patterns in ontology learning. We propose a preliminary set of lexico-semantic patterns designed for the Czech language to learn new relations between concepts in the related domain ontology in a semi-supervised approach. We utilize data from the urban planning and development domain to evaluate the introduced technique. As a partial prototypical implementation of the stack, we present Annotace, a text annotation service that provides links between the ontology model and the textual documents in Czech.

Druh

D - Stať ve sborníku

CEP obor

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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/CK01000204" target="_blank" >CK01000204: Zvýšení efektivity plánování a provádění údržby dopravních letadel</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

The Semantic Web 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31–June 4, 2020, Proceedings

ISBN

978-3-030-49460-5

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

16

Strana od-do

138-153

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Crete

Datum konání akce

2. 6. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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