Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/15038
Title: Ontology based optimization techniques for information retrieval
Researcher: Sridevi U K
Guide(s): Nagaveni, N.
Keywords: Ontology, optimization techniques, information retrieval
Upload Date: 15-Jan-2014
University: Anna University
Completed Date: 
Abstract: Searching the Web has become more challenging due to the rapid growth in information. The documents contain much valuable knowledge about a particular domain. The ontology can be used as main resource to understand the textual information contained within the documents. The objective of the research is to define a model for the annotation and retrieval using optimization techniques. This research shows how to apply ontology based annotation method to improve the retrieval. The quality of the solution obtained can be improved by using annotated weights and optimized clustering algorithm. Another objective is to extract relevant concept from the corpus. The ontology population generates the new instance and results in the semantic annotation of document. The main goal of information extraction is to retrieve the relevant information from the document and to create an instance of ontology. The main goal of ontology-driven information retrieval is to enhance search by making use of available semantic annotations and their underlining ontologies. The objective of the particle swarm optimization clustering algorithm is to discover the proper centroids of clusters for minimizing the intra-cluster distance as well as maximizing the distance between clusters. This study investigates the application of fuzzy particle swarm optimization in document clustering. The main objective is to apply the fuzzy particle swarm optimization clustering method on the semantically annotated documents. A fuzzy particle swarm optimization combined with ontology model of clustering knowledge documents is presented and compared to the traditional vector space model. It also overcomes the problems existing in the vector space model commonly used for clustering. The proposed ontology framework provides improved performance and better clustering compared to the traditional vector space model. The increase in F-measure is achieved when ontology as the distance measure in fuzzy particle swarm optimization. The improvement of 11% is achieved by ont
Pagination: xix, 136
URI: http://hdl.handle.net/10603/15038
Appears in Departments:Faculty of Science and Humanities

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01_title.pdfAttached File32.25 kBAdobe PDFView/Open
02_certificates.pdf1.4 MBAdobe PDFView/Open
03_abstract.pdf39.89 kBAdobe PDFView/Open
04_acknowledgement.pdf15.33 kBAdobe PDFView/Open
05_contents.pdf73.65 kBAdobe PDFView/Open
06_chapter 1.pdf100.51 kBAdobe PDFView/Open
07_chapter 2.pdf560.34 kBAdobe PDFView/Open
08_chapter 3.pdf252.89 kBAdobe PDFView/Open
09_chapter 4.pdf153.86 kBAdobe PDFView/Open
10_chapter 5.pdf136.82 kBAdobe PDFView/Open
11_chapter 6.pdf167.21 kBAdobe PDFView/Open
12_chapter 7.pdf35.5 kBAdobe PDFView/Open
13_appendix 1.pdf63.79 kBAdobe PDFView/Open
14_references.pdf112.45 kBAdobe PDFView/Open
15_publications.pdf61.74 kBAdobe PDFView/Open
16_vitae.pdf29.27 kBAdobe PDFView/Open


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