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A Study on Web Ontologies-Based Reengineering Software for Cloud Computing Using Probabilistic Topic Models


Affiliations
1 PRIST University, Vallam, Thanjavur, India
2 P.R. Engineering College, Vallam, Thanjavur, India
     

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Customer Relationship Management, is an area where cloud computing can really shine. Applications for CRM can help a business organize and track its contacts with customers. Thus knowledge discovery and management plays vital role in these area. In this article we propose a new approach for automatic learning of terminological ontologies from text corpus based on probabilistic topic models .In our approach topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-document interpreted in terms of probability distributions. This article proposed a method to create and handle ontology object model. The work has also involve testing the performance of the proposed algorithm. Thus compared to manual approaches, the automated building of ontology has the advantages of fast development, cost effectiveness, and being resistant to obsolescence.

Keywords

Cloud Computing, Web Ontology, Ontology Generation, Probabilistic Topic Models, CRM.
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  • A Study on Web Ontologies-Based Reengineering Software for Cloud Computing Using Probabilistic Topic Models

Abstract Views: 258  |  PDF Views: 4

Authors

R. Rathika
PRIST University, Vallam, Thanjavur, India
M. Ambika
P.R. Engineering College, Vallam, Thanjavur, India

Abstract


Customer Relationship Management, is an area where cloud computing can really shine. Applications for CRM can help a business organize and track its contacts with customers. Thus knowledge discovery and management plays vital role in these area. In this article we propose a new approach for automatic learning of terminological ontologies from text corpus based on probabilistic topic models .In our approach topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-document interpreted in terms of probability distributions. This article proposed a method to create and handle ontology object model. The work has also involve testing the performance of the proposed algorithm. Thus compared to manual approaches, the automated building of ontology has the advantages of fast development, cost effectiveness, and being resistant to obsolescence.

Keywords


Cloud Computing, Web Ontology, Ontology Generation, Probabilistic Topic Models, CRM.