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Erschienen in:
Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Information Extraction for Learning Expressive Ontologies

verfasst von : Giulio Petrucci

Erschienen in: The Semantic Web. Latest Advances and New Domains

Verlag: Springer International Publishing

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Abstract

Ontologies are used to represent knowledge in a formal and unambiguous way, facilitating its reuse and sharing among people and computer systems. A large amount of knowledge is traditionally available in unstructured text sources and manually encoding their content into a formal representation is costly and time-consuming. Several methods have been proposed to support ontology engineers in the ontology building process, but they mostly turned out to be inadequate for building rich and expressive ontologies. We propose some concrete research directions for designing an effective methodology for semi-supervised ontology learning. This methodology will integrate a new axiom extraction technique which exploits several features of the text corpus.

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Vorheriges Kapitel The Design and Implementation of Semantic Web-Based Architecture for Augmented Reality Browser
Nächstes Kapitel A Scalable Adaptive Method for Complex Reasoning Over Semantic Data Streams
Fußnoten
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Metadaten
Titel
Information Extraction for Learning Expressive Ontologies
verfasst von
Giulio Petrucci
Copyright-Jahr
2015
Buch
The Semantic Web. Latest Advances and New Domains

Print ISBN: 978-3-319-18817-1

Electronic ISBN: 978-3-319-18818-8

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-18818-8_47

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