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FCA-Based Ontology Learning From Unstructured Textual Data
: Springer
Date Issued
2018-12-20
Abstract
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.
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
Event name
Mining Intelligence and Knowledge Exploration (MIKE)
Location
Cluj - Napoca, Romania
Other version
https://link.springer.com/chapter/10.1007/978-3-030-05918-7_1
Publication type
conference paper
