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A Web-Based Method for Ontology Population

A Web-Based Method for Ontology Population

Hilário Oliveira, Rinaldo Lima, João Gomes, Fred Freitas, Rafael Dueire Lins, Steven J. Simske, Marcelo Riss
Copyright: © 2016 |Pages: 22
ISBN13: 9781466687516|ISBN10: 1466687517|EISBN13: 9781466687523
DOI: 10.4018/978-1-4666-8751-6.ch025
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MLA

Oliveira, Hilário, et al. "A Web-Based Method for Ontology Population." Mobile Computing and Wireless Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 565-586. https://doi.org/10.4018/978-1-4666-8751-6.ch025

APA

Oliveira, H., Lima, R., Gomes, J., Freitas, F., Lins, R. D., Simske, S. J., & Riss, M. (2016). A Web-Based Method for Ontology Population. In I. Management Association (Ed.), Mobile Computing and Wireless Networks: Concepts, Methodologies, Tools, and Applications (pp. 565-586). IGI Global. https://doi.org/10.4018/978-1-4666-8751-6.ch025

Chicago

Oliveira, Hilário, et al. "A Web-Based Method for Ontology Population." In Mobile Computing and Wireless Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 565-586. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8751-6.ch025

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Abstract

The Semantic Web, proposed by Berners-Lee, aims to make explicit the meaning of the data available on the Internet, making it possible for Web data to be processed both by people and intelligent agents. The Semantic Web requires Web data to be semantically classified and annotated with some structured representation of knowledge, such as ontologies. This chapter proposes an unsupervised, domain-independent method for extracting instances of ontological classes from unstructured data sources available on the World Wide Web. Starting with an initial set of linguistic patterns, a confidence-weighted score measure is presented integrating distinct measures and heuristics to rank candidate instances extracted from the Web. The results of several experiments are discussed achieving very encouraging results, which demonstrate the feasibility of the proposed method for automatic ontology population.

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