Open Access
Peer-Reviewed
Original Research
Automatic Generation of Ontologies: a Hierarchical Word Clustering Approach
Abstract
In the context of globalization, companies need to capitalize on their knowledge. The knowledge of a company is present in two forms tacit and explicit. Explicit knowledge represents all formalized information i.e all documents (pdf, words ...). Tacit knowledge is present in documents and mind of employees, this kind of knowledge is not formalized, it needs a reasoning process to discover it. The approach proposed focus on extracting tacit knowledge from textual documents. In this paper, we propose hierarchical word clustering as an improvement of word clusters generated in previous work, we also proposed an approach to extract relevant bigrams and trigrams. We use Reuters-21578 corpus to validate our approach. Our global work aims to ease the automatic building of ontologies.
Keywords
Knowledge Management
Ontologies
Word Clustering
Experience Feedback
Declarations & Ethics
Funding:
This research received academic dissemination support through ESCAP / JournalsHub publishing programs.
Conflicts of Interest:
The authors declare no competing financial or institutional interests.
Peer Review:
Double-blind peer reviewed by international subject specialists.
License:
Creative Commons Attribution 4.0 International (CC BY 4.0).
How to Cite This Article
APA / MLA / BibTeX
Sellah, et al. (2018). Automatic Generation of Ontologies: a Hierarchical Word Clustering Approach. IADIS International Journal on Computer Science and Information Systems, 13(2). https://doi.org/10.33965/ijcsis_2018_v13i2_07
Sellah, et al. "Automatic Generation of Ontologies: a Hierarchical Word Clustering Approach." IADIS International Journal on Computer Science and Information Systems, vol. 13, no. 2, 2018. https://doi.org/10.33965/ijcsis_2018_v13i2_07
Sellah, et al. "Automatic Generation of Ontologies: a Hierarchical Word Clustering Approach." IADIS International Journal on Computer Science and Information Systems 13, no. 2 (2018). https://doi.org/10.33965/ijcsis_2018_v13i2_07