Friday 29th of March 2024
 

Refined Ontology Model for Content Anatomy and Topic Summarization


Mekala and Chandra Sekar

When the performance of any information processing system can be enhanced by the concept of ontologies, domain specific terms enclosing wealthy and defined semantics. Research has been accomplished with the help of variety of resources on automatic ontology construction. Each of these resources has different qualities that have need of special approaches to term and relationship extraction. On the consideration of terminological resources, semantic structure of ontology construction facilitates the NLP (Natural Language Processing) that extracts terms and relationships. Generally in this phase there can be a problem in that many relationships are incorrectly defined or applied excessively. For that reason, extracting ontological relationships from documents necessitates data cleaning and refinement of semantic relationships. In our research we provide the automatic term relationship and refinement ontology construction for the content anatomy and topic summarization. Where the automatic topic extraction mechanism will be done based on the significant score computation and the highest score will be the topics. Our proposed system supports effective joint inference approach, which simultaneously constructs the ISA (is a) and HASA (Has a)-tree, while mapping Topic models to WordNet, achieves the best performance. To end with, we estimate our ontology-based topic summarization results that formulate exploit of similarity-based metrics first enlarged for automatic term relationship findings and refinement of semantic relationships. The experimental result shows that the proposed system produce the better summarization result when compared with the existing methods.

Keywords: Semantic Relationship Refinement, Noun Phrase Analysis, Semantic Web, Ontology, ISA (is a) &HASA (Has a)-tree, WordNet, Natural Language Processing, Automatic term relationship detection, Information Extraction.

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ABOUT THE AUTHORS

Mekala
done M.Sc(CS), from bharathiyar University coimbatore in 2006, completed M.Phil.(CS), from bharathidhasan University,Trichy in 2008. Received MCA, from Periyar University Salem in 2011. Currently working as a Asst. Professor in Dept. of Computer Science, Hindusthan College of arts and science, Coimbatore. Her research area is data mining.

Chandra Sekar
received his Ph.D. degree from Periyar University, Salem, TN, India. He has been working as Associate Professor at Dept. of Computer Science, Periyar University, Salem – 636 011, Tamil Nadu, India. His research interest includes Wireless networking, Mobile computing, Computer Communication and Networks. He was a Research guide at various universities in India. He has been published more than 50 research papers at various National / International Journals.


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