FCA-Based Ontology Learning From Unstructured Textual Data

Jabbari, Simin

doi:10.1007/978-3-030-05918-7_1

FCA-Based Ontology Learning From Unstructured Textual Data

Auteur(s)

Jabbari, Simin

Institut du management de l'information

Maison d'édition

: Springer

Date de parution

2018-12-20

Mots-clés

Résumé

Ontologies have been frequently used for representing a domain knowledge. It has a lot of applications in semantic knowledge extraction. However, learning ontologies especially from unstructured data is a difficult yet an interesting challenge. In this paper, we introduce a pipeline for learning ontology from a text corpora in a semi-automated fashion using Natural Language Processing (NLP) and Formal Concept
Analysis (FCA). We apply our proposed method on a small given corpus that consists of some news documents in IT and pharmaceutical domain. We then discuss the potential applications of the proposed model and ideas on how to improve it even further.

Notes

, 2018

Nom de l'événement

Mining Intelligence and Knowledge Exploration (MIKE)

Lieu

Cluj - Napoca, Romania

Identifiants

https://libra.unine.ch/handle/123456789/26903

_

10.1007/978-3-030-05918-7_1

Autre version

https://link.springer.com/chapter/10.1007/978-3-030-05918-7_1