Ontology Learning

B 2006

Ontology Learning

NOVÁČEK, Vít

Základní údaje

Originální název

Ontology Learning

Autoři

NOVÁČEK, Vít

Vydání

Brno, 65 s. Diploma Thesis, 2006

Nakladatel

Faculty of Informatics, Masaryk University

Další údaje

Typ výsledku

Odborná kniha

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

artificial intelligence; natural language processing; ontology; ontology acquisition; knowledge representation; text mining; knowledge extraction; uncertainty represe ntation

Štítky

Artificial Intelligence, knowledge extraction, knowledge representation, natural language processing, ontology, ontology acquisition, text mining, uncertainty represe ntation

Změněno: 25. 10. 2006 12:01, doc. Mgr. Bc. Vít Nováček, PhD

Anotace

V originále

Ontology learning is one of the essential topics in the scope of an important ar ea of current computer science and artificial intelligence -- the upcoming Semantic Web. As the Semanti c Web idea comprises semantically annotated descendant of the current world wide web a nd related tools and resources, the need of vast and reliable knowledge repositories is obvious. Onto logies present well defined, straightforward and standardised form of these repositories. There are many poss ible utilisations of ontologies -- from automatic annotation of web resources to domain representa tion and reasoning tasks. However, the ontology creation process is very expensive, time-consuming and uno bjective when performed manually. So a framework for automatic acquisition of ontologies would be very a dvantageous. In this work we present such a framework called OLE (an acronym for Ontology LEarning) a nd current results of its application. The main relevant topics, state of the art methods and techniques r elated to ontology acquisition are discussed as a part of theoretical background for the presentati on of the OLE framework and respective results. Moreover, we describe also preliminary results of progre ssive research in the area of uncertain fuzzy ontology representation that will provide us with natura l and reasonable instruments for dealing with inconsistencies in empiric data as well as for reas oning. Main future milestones of the ongoing research are debated as well.

Návaznosti

1ET100300419, projekt VaV

Název: Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu

Investor: Akademie věd ČR, Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu

Citovat

NOVÁČEK, Vít. Ontology Learning. Brno: Faculty of Informatics, Masaryk University, 2006, 65 s. Diploma Thesis.

@book{599544,
   author = {Nováček, Vít},
   address = {Brno},
   keywords = {artificial intelligence; natural language processing; ontology; ontology acquisition; knowledge representation; text mining; knowledge extraction; uncertainty represe
ntation},
   location = {Brno},
   publisher = {Faculty of Informatics, Masaryk University},
   title = {Ontology Learning},
   url = {http://nlp.fi.muni.cz/projects/ole/pubs.html},
   year = {2006}
}

TY  - BOOK
ID  - 599544
AU  - Nováček, Vít
PY  - 2006
TI  - Ontology Learning
VL  - Diploma Thesis
PB  - Faculty of Informatics, Masaryk University
CY  - Brno
KW  - artificial intelligence
KW  - natural language processing
KW  - ontology
KW  - ontology acquisition
KW  - knowledge representation
KW  - text mining
KW  - knowledge extraction
KW  - uncertainty represe
ntation
UR  - http://nlp.fi.muni.cz/projects/ole/pubs.html
N2  - Ontology learning is one of the essential topics in the scope of an important ar ea of current computer science and artificial intelligence -- the upcoming Semantic Web. As the Semanti c Web idea comprises semantically annotated descendant of the current world wide web a nd related tools and resources, the need of vast and reliable knowledge repositories is obvious. Onto logies present well defined, straightforward and standardised form of these repositories. There are many poss ible utilisations of ontologies -- from automatic annotation of web resources to domain representa tion and reasoning tasks. However, the ontology creation process is very expensive, time-consuming and uno bjective when performed manually. So a framework for automatic acquisition of ontologies would be very a dvantageous. In this work we present such a framework called OLE (an acronym for Ontology LEarning) a nd current results of its application. The main relevant topics, state of the art methods and techniques r elated to ontology acquisition are discussed as a part of theoretical background for the presentati on of the OLE framework and respective results. Moreover, we describe also preliminary results of progre ssive research in the area of uncertain fuzzy ontology representation that will provide us with natura l and reasonable instruments for dealing with inconsistencies in empiric data as well as for reas oning. Main future milestones of the ongoing research are debated as well.
ER  -

NOVÁČEK, Vít. \textit{Ontology Learning}. Brno: Faculty of Informatics, Masaryk University, 2006, 65 s. Diploma Thesis.

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