Lightweight Domain Ontology Learning from Texts: Graph Theory-Based Approach Using Wikipedia

From Wikipedia Quality
Revision as of 23:29, 28 May 2019 by Hanna (talk | contribs) (Overview: Lightweight Domain Ontology Learning from Texts: Graph Theory-Based Approach Using Wikipedia)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

Lightweight Domain Ontology Learning from Texts: Graph Theory-Based Approach Using Wikipedia - scientific work related to Wikipedia quality published in 2014, written by Khalida Bensidi Ahmed, Adil Toumouh and Dominic Widdows.

Overview

Ontology engineering is the backbone of the semantic web. However, the construction of formal ontologies is a tough exercise which requires time and heavy costs. Ontology learning is thus a solution for this requirement. Since texts are massively available everywhere, making up of experts' knowledge and their know-how, it is of great value to capture the knowledge existing within such texts. Authors approach is thus the kind of research work that answers the challenge of creating concepts' hierarchies from textual data taking advantage of the Wikipedia encyclopaedia to achieve some good-quality results. This paper presents a novel approach which essentially uses plain text Wikipedia instead of its categorical system and works with a simplified algorithm to infer a domain taxonomy from a graph.