Bibliographic Details
| Title: |
Terminological ontology learning and population using latent Dirichlet allocation. |
| Authors: |
Colace, Francesco1 fcolace@unisa.it, De Santo, Massimo1 desanto@unisa.it, Greco, Luca1 lgreco@unisa.it, Amato, Flora2 flora.amato@unisa.it, Moscato, Vincenzo2 vmoscato@unisa.it, Picariello, Antonio2 picus@unisa.it |
| Source: |
Journal of Visual Languages & Computing. Dec2014, Vol. 25 Issue 6, p818-826. 9p. |
| Subjects: |
Machine learning, Data structures, Dirichlet problem, Natural language processing, Semantic Web |
| Abstract: |
The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
