Topic extraction for ontology learning

Rizoiu, MA; Velcin, J

Topic extraction for ontology learning

Rizoiu, MA Velcin, J

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Publisher:: IGI Global
Publication Type:: Chapter
Citation:: Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances, 2011, pp. 38-60
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Rizoiu, MA
dc.contributor.author	Velcin, J
dc.date.accessioned	2021-01-22T06:32:15Z
dc.date.available	2021-01-22T06:32:15Z
dc.date.issued	2011-12-01
dc.identifier.citation	Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances, 2011, pp. 38-60
dc.identifier.isbn	9781609606251
dc.identifier.uri	http://hdl.handle.net/10453/145559
dc.description.abstract	This chapter addresses the issue of topic extraction from text corpora for ontology learning. The first part provides an overview of some of the most significant solutions present today in the literature. These solutions deal mainly with the inferior layers of the Ontology Learning Layer Cake. They are related to the challenges of the Terms and Synonyms layers. The second part shows how these pieces can be bound together into an integrated system for extracting meaningful topics. While the extracted topics are not proper concepts as yet, they constitute a convincing approach towards concept building and therefore ontology learning. This chapter concludes by discussing the research undertaken for filling the gap between topics and concepts as well as perspectives that emerge today in the area of topic extraction. © 2011, IGI Global.
dc.language	en
dc.publisher	IGI Global
dc.relation.ispartof	Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances
dc.relation.isbasedon	10.4018/978-1-60960-625-1.ch003
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Topic extraction for ontology learning
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-22T06:31:41Z
pubs.publication-status	Published

Abstract:

This chapter addresses the issue of topic extraction from text corpora for ontology learning. The first part provides an overview of some of the most significant solutions present today in the literature. These solutions deal mainly with the inferior layers of the Ontology Learning Layer Cake. They are related to the challenges of the Terms and Synonyms layers. The second part shows how these pieces can be bound together into an integrated system for extracting meaningful topics. While the extracted topics are not proper concepts as yet, they constitute a convincing approach towards concept building and therefore ontology learning. This chapter concludes by discussing the research undertaken for filling the gap between topics and concepts as well as perspectives that emerge today in the area of topic extraction. © 2011, IGI Global.

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http://hdl.handle.net/10453/145559