Semantic Similarity Measures for Knowledge Engineering: Experiments on UMLS, WordNet and Biomedical Corpus - Couverture souple

Pesaranghader, Ahmad; Muthaiyah, Saravanan

 
9783659341267: Semantic Similarity Measures for Knowledge Engineering: Experiments on UMLS, WordNet and Biomedical Corpus
Couverture souple
ISBN 10 : 3659341266 ISBN 13 : 9783659341267
Editeur : LAP LAMBERT Academic Publishing, 2013

Synopsis

Knowledge management has been considered in the past decades applying techniques to cope with organizing data, information and knowledge. It implements methods to manage knowledge on papers as well as digital ones. Meanwhile, birth of the World Wide Web despite all of its advantages has initiated a number of issues for the researchers due to massiveness of the data on the Web. Therefore, the new knowledge engineering techniques must be automated to save time and effort of man power with considering the Web with a shared understanding of the data among all of its components. To achieve accurate and integrated definition of all available data, machines need to make a unique understanding of all discrete data sources. This book is aimed at presenting existing Measures of Semantic Similarity for resolving foregoing issue. These measures are also useful in tasks such as text categorizing, machine translation and information retrieval. Furthermore, this book introduces two new normalized functions for measuring semantic similarity between two concepts based on first and second order context and information content vectors computed from MEDLINE as the biomedical corpus, UMLS and WordNet.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Knowledge management has been considered in the past decades applying techniques to cope with organizing data, information and knowledge. It implements methods to manage knowledge on papers as well as digital ones. Meanwhile, birth of the World Wide Web despite all of its advantages has initiated a number of issues for the researchers due to massiveness of the data on the Web. Therefore, the new knowledge engineering techniques must be automated to save time and effort of man power with considering the Web with a shared understanding of the data among all of its components. To achieve accurate and integrated definition of all available data, machines need to make a unique understanding of all discrete data sources. This book is aimed at presenting existing Measures of Semantic Similarity for resolving foregoing issue. These measures are also useful in tasks such as text categorizing, machine translation and information retrieval. Furthermore, this book introduces two new normalized functions for measuring semantic similarity between two concepts based on first and second order context and information content vectors computed from MEDLINE as the biomedical corpus, UMLS and WordNet.

Biographie de l'auteur

Ahmad Pesaranghader is a researcher in the field of Computational Intelligence and Computational Linguistics. He holds a Bachelor's degree in Computer Software Engineering. He has also obtained a Master's degree in Knowledge Management with Multimedia from Multimedia University. His primary research interests are Social and Semantic Web.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2013
Langue
anglais
ISBN 10 
3659341266
ISBN 13 
9783659341267
Reliure
Broché
Nombre de pages
220
Coordonnées du fabricant
non disponible
Personne responsable
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