SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction

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Anna Saro Vijendran 1,* Deepa .C 2

1. Department of MCA, SNR Sons College, Coimbatore- 641 006, India

2. Department of Information Technology, SNR Sons College, Coimbatore- 641 006, India

* Corresponding author.


Received: 20 Feb. 2014 / Revised: 14 Jun. 2014 / Accepted: 6 Sep. 2014 / Published: 8 Dec. 2014

Index Terms

Web Structure Mining, Ontology, Semantic Annotation, Block Acquiring Page Segmentation (BAPS), Semantic Annotation Based Clustering (SANB), Semantic Based Clustering (SEB)


Data mining is a hype-word and its major goal is to extract the information from the dataset and convert it into readable format. Web mining is one of the applications of data mining which helps to extract the web page. Personalized image was retrieved in existing systems by using tag-annotation-demand ranking for image retrieval (TAD) where image uploading, query searching, and page refreshing steps were taken place. In the proposed work, both the image and web page are retrieved by several techniques. Two major steps are followed in this work, where the primary step is server database upload. Herein, database for both image and content are stored using block acquiring page segmentation (BAPS). The subsequent step is to extract the image and content from the respective server database. The subsequent database is further applied into semantic annotation based clustering (SANB) (for image) and semantic based clustering (SEB) (for content). The experimental results show that the proposed approach accurately retrieves both the images and relevant pages.

Cite This Paper

Anna Saro Vijendran, Deepa .C, "SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.1, pp.41-47, 2015. DOI:10.5815/ijitcs.2015.01.05


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