Issue |
MATEC Web Conf.
Volume 135, 2017
8th International Conference on Mechanical and Manufacturing Engineering 2017 (ICME’17)
|
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Article Number | 00075 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.1051/matecconf/201713500075 | |
Published online | 20 November 2017 |
Axiomatic Ontology Learning Approaches for English Translation of the Meaning of Quranic Texts
1
Centre for Artificial Intelligence, Faculty of Technology and Information Science, Universiti Kebangsaan Malaysia
2
Centre for Artificial Intelligence, Faculty of Technology and Information Science, Universiti Kebangsaan Malaysia
* Corresponding author: saidah@ukm.edu.my
Ontology learning (OL) is the computational task of generating a knowledge base in the form of an ontology, given an unstructured corpus in natural language (NL). While most works in the field of ontology learning have been primarily based on a statistical approach to extract lightweight OL, very few attempts have been made to extract axiomatic OL (called heavyweight OL) from NL text documents. Axiomatic OL supports more precise formal logic-based reasoning when compared to lightweight OL. Lexico-syntactic pattern matching and statisticsal one cannot lead to very accurate learning, mostly because of several linguistic nuances in the NL. Axiomatic OL is an alternative methodology that has not been explored much, where a deep linguistics analysis in computational linguistics is used to generate formal axioms and definitions instead of simply inducing a taxonomy. The ontology that is created not only stores the information about the application domain in explicit knowledge, but also can deduce the implicit knowledge from this ontology. This research will explore the English translation of the meaning of Quranic texts.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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