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Loose Method for Pattern Classification in Wikipedia using Duality Theorem for Knowledge Acquisition in Neigbouring Words

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 2
Year of Publication: 2015
Authors:
Enikuomehin Toyin
Akerele Olubunmi
10.5120/21670-4751

Enikuomehin Toyin and Akerele Olubunmi. Article: Loose Method for Pattern Classification in Wikipedia using Duality Theorem for Knowledge Acquisition in Neigbouring Words. International Journal of Computer Applications 122(2):4-8, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Enikuomehin Toyin and Akerele Olubunmi},
	title = {Article: Loose Method for Pattern Classification in Wikipedia using Duality Theorem for Knowledge Acquisition in Neigbouring Words},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {2},
	pages = {4-8},
	month = {July},
	note = {Full text available}
}

Abstract

In this paper, we present an approach for structural classification of taxonomies for knowledge acquisition from Wikipedia using standard loose frameworks. Knowledge mapped from WordNet are assigned to corresponding patterns in Wikipedia such that the syse structure are automatically acquired for related patterns and then used for knowledge generation, achievable through Learning. The paper considers the theory of duality principle as posed in Hilbert spaces to describe the operation of two terms related by their linguistic classifications such as hyponyms. Results show that knowledge can be acquired with well formulated pattern, however a lot of gaps still exist which can be solved using manual approaches as that seems to be more efficient based on the experiment conducted.

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