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Topic Extraction for Ontology Learning

Topic Extraction for Ontology Learning

Marian-Andrei RIZOIU, Julien VELCIN
Copyright: © 2011 |Pages: 23
ISBN13: 9781609606251|ISBN10: 1609606256|EISBN13: 9781609606268
DOI: 10.4018/978-1-60960-625-1.ch003
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MLA

RIZOIU, Marian-Andrei, and Julien VELCIN. "Topic Extraction for Ontology Learning." Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances, edited by Wilson Wong, et al., IGI Global, 2011, pp. 38-60. https://doi.org/10.4018/978-1-60960-625-1.ch003

APA

RIZOIU, M. & VELCIN, J. (2011). Topic Extraction for Ontology Learning. In W. Wong, W. Liu, & M. Bennamoun (Eds.), Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances (pp. 38-60). IGI Global. https://doi.org/10.4018/978-1-60960-625-1.ch003

Chicago

RIZOIU, Marian-Andrei, and Julien VELCIN. "Topic Extraction for Ontology Learning." In Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances, edited by Wilson Wong, Wei Liu, and Mohammed Bennamoun, 38-60. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-625-1.ch003

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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.

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