A Domain-Independent Hybrid Approach for Automatic Taxonomy Induction

Zafar, Bushra; Qamar, Usman; Imran, Ayesha

doi:10.1109/pdcat.2016.085

Cited by 1 publication

(1 citation statement)

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“…Taxonomic relations in an ontology are represented by the hierarchical arrangement of concepts based on hypernym/hyponym (i.e., broader/narrower) relations. The building of the taxonomic relations, hence, is considered as the first step in the ontology construction and corresponds to identifying hypernym/hyponym relation between the concepts [33,34,40].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

Doan

Arch-int

2019

IAJIT

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Nowadays, ontologies have been exploited in many current applications due to the abilities in representing knowledge and inferring new knowledge. However, the manual construction of ontologies is tedious and time-consuming. Therefore, the automated ontology construction from text has been investigated. The extraction of taxonomic relations between concepts is a crucial step in constructing domain ontologies. To obtain taxonomic relations from a text corpus, especially when the data is deficient, the approach of using the web as a source of collective knowledge (a.k.a web-based approach) is usually applied. The important challenge of this approach is how to collect relevant knowledge from a large amount of web pages. To overcome this issue, we propose a framework that combines Word Sense Disambiguation (WSD) and web approach to extract taxonomic relations from a domain-text corpus. This framework consists of two main stages: concept extraction and taxonomic-relation extraction. Concepts acquired from the concept-extraction stage are disambiguated through WSD module and passed to stage of extraction taxonomic relations afterward. To evaluate the efficiency of the proposed framework, we conduct experiments on datasets about two domains of tourism and sport. The obtained results show that the proposed method is efficient in corpora which are insufficient or have no training data. Besides, the proposed method outperforms the state of the art method in corpora having high WSD results.

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Section: Introductionmentioning

confidence: 99%