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Probabilistic Models for Ontology Learning: Transitivity in semantic relation learning - Couverture souple
Synopsis
Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning such as semantic networks of words or concepts are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Our main goal is to contribute practically to the research on semantic networks learning models by covering different aspects of the task. We propose a novel probabilistic model for learning semantic networks that expands existing semantic networks taking into accounts both corpus-extracted evidences and the structure of the generated semantic networks. The model exploits structural properties of target relations such as transitivity during learning. Our model presents some innovations in estimating the probabilities. We then propose two extensions of our probabilistic model: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that puts human validations in the learning loop.
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À propos de l'auteur
She has received the PhD in Computer Science and Engineering at the University of Rome "Tor Vergata".She is currently a researcher at University of Rome "GUGLIELMO MARCONI" and she has a collaboration with DigitPA.Her main research interests in the area of NLP include probabilistic taxonomy learning, ontology learning, and knowledge management.
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Probabilistic Models for Ontology Learning
Edité par
LAP LAMBERT Academic Publishing Jul 2012, 2012
ISBN 10 : 3659171409
ISBN 13 : 9783659171406
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning such as semantic networks of words or concepts are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Our main goal is to contribute practically to the research on semantic networks learning models by covering different aspects of the task. We propose a novel probabilistic model for learning semantic networks that expands existing semantic networks taking into accounts both corpus-extracted evidences and the structure of the generated semantic networks. The model exploits structural properties of target relations such as transitivity during learning. Our model presents some innovations in estimating the probabilities. We then propose two extensions of our probabilistic model: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that puts human validations in the learning loop. 132 pp. Englisch. N° de réf. du vendeur 9783659171406
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Probabilistic Models for Ontology Learning : Transitivity in semantic relation learning
Edité par
LAP LAMBERT Academic Publishing, 2012
ISBN 10 : 3659171409
ISBN 13 : 9783659171406
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning such as semantic networks of words or concepts are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Our main goal is to contribute practically to the research on semantic networks learning models by covering different aspects of the task. We propose a novel probabilistic model for learning semantic networks that expands existing semantic networks taking into accounts both corpus-extracted evidences and the structure of the generated semantic networks. The model exploits structural properties of target relations such as transitivity during learning. Our model presents some innovations in estimating the probabilities. We then propose two extensions of our probabilistic model: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that puts human validations in the learning loop. N° de réf. du vendeur 9783659171406
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Probabilistic Models for Ontology Learning
Edité par
LAP LAMBERT Academic Publishing, 2012
ISBN 10 : 3659171409
ISBN 13 : 9783659171406
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Fallucchi FrancescaShe has received the PhD in Computer Science and Engineering at the University of Rome Tor Vergata .She is currently a researcher at University of Rome GUGLIELMO MARCONI and she has a collaboration with DigitPA. N° de réf. du vendeur 5136779
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Probabilistic Models for Ontology Learning
Edité par
LAP LAMBERT Academic Publishing Jul 2012, 2012
ISBN 10 : 3659171409
ISBN 13 : 9783659171406
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning such as semantic networks of words or concepts are knowledge repositories used in a variety of applications. To be effectively used, these networks have to be large or, at least, adapted to specific domains. Our main goal is to contribute practically to the research on semantic networks learning models by covering different aspects of the task. We propose a novel probabilistic model for learning semantic networks that expands existing semantic networks taking into accounts both corpus-extracted evidences and the structure of the generated semantic networks. The model exploits structural properties of target relations such as transitivity during learning. Our model presents some innovations in estimating the probabilities. We then propose two extensions of our probabilistic model: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that puts human validations in the learning loop.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 132 pp. Englisch. N° de réf. du vendeur 9783659171406
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Probabilistic Models for Ontology Learning | Transitivity in semantic relation learning
Edité par
LAP LAMBERT Academic Publishing, 2012
ISBN 10 : 3659171409
ISBN 13 : 9783659171406
Neuf
Taschenbuch
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Probabilistic Models for Ontology Learning | Transitivity in semantic relation learning | Francesca Fallucchi (u. a.) | Taschenbuch | 132 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659171406 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 106359763
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