Ontology Learning and Population from Text
Algorithms, Evaluation and Applications
Published on 29. October 2010
Book
Paperback/Softback
XXVIII, 347 pages
978-1-4419-4032-2 (ISBN)
Description
In the last decade, ontologies have received much attention within computer science and related disciplines, most often as the semantic web. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications discusses ontologies for the semantic web, as well as knowledge management, information retrieval, text clustering and classification, as well as natural language processing.
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications is structured for research scientists and practitioners in industry. This book is also suitable for graduate-level students in computer science.
More details
Edition
1st ed. Softcover of orig. ed. 2006
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XXVIII, 347 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 21 mm
Weight
569 gr
ISBN-13
978-1-4419-4032-2 (9781441940322)
DOI
10.1007/978-0-387-39252-3
Schweitzer Classification
Other editions
Additional editions
Book
10/2006
Springer
€106.99
Shipment within 5-7 days
Person
Philipp Cimiano is professor of computer science at Bielefeld University and head of the Semantic Computing group, affiliated with the Cluster of Excellence on Cognitive Interaction Technology (CITEC). He received his doctoral degree in applied computer science from the University of Karlsruhe (now KIT) on the topic of learning ontologies from text. He has a wide range of publications in the areas of natural language processing, ontology learning, knowledge acquisition and representation, and the Semantic Web. He was nominated one of AI's 10 to Watch by the IEEE Intelligent Systems Magazine in 2008, an award given to the top 10 young researchers in the field of artificial intelligence worldwide.
Content
Preliminaries.- Ontologies.- Ontology Learning from Text.- Basics.- Datasets.- Methods and Applications.- Concept Hierarchy Induction.- Learning Attributes and Relations.- Population.- Applications.- Conclusion.- Contribution and Outlook.- Concluding Remarks.