Web Search Query Rewriting

Since the results from Semantic Web search engines are highly structured XML documents, they cannot be efficiently visualized with traditional explorers. Therefore, the Semantic Web calls for a new generation of search query visualizers that can rely on document metadata. This paper introduces such a visualization system called WebContent Visualizer that is used to display and browse search engine results. The visualization is organized into three levels: (1) Carousels contain documents with the same ranking, (2) carousels are piled into stacks, one for each date, and (3) these stacks are organized along a meta-carousel to display the results for several dates. Carousel stacks are piles of local carousels with increasing radii to visualize the ranks of classes. For document comparison, colored links connect documents between neighboring classes on the basis of shared entities. Based on these techniques, the interface is made of three collaborative components: an inspector window, a visualization panel, and a detailed dialog component. With this architecture, the system is intended to offer an efficient way to explore the results returned by Semantic Web search engines.

Download Full-text

Efficient Document Clustering for Web Search Result

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.3.14494 ◽

2018 ◽

Vol 7 (3.3) ◽

pp. 90

Author(s):

Sumathi Rani Manukonda ◽

Asst.Prof Kmit ◽

Narayanguda . ◽

Hyderabad . ◽

Nomula Divya ◽

...

Keyword(s):

Hierarchical Clustering ◽

Web Search ◽

Traditional Approach ◽

Document Clustering ◽

Nearest Neighbors ◽

Clustering Methods ◽

Search Query ◽

Semantic Approach ◽

Search Result ◽

Distinct Method

Clustering the document in data mining is one of the traditional approach in which the same documents that are more relevant are grouped together. Document clustering take part in achieving accuracy that retrieve information for systems that identifies the nearest neighbors of the document. Day to day the massive quantity of data is being generated and it is clustered. According to particular sequence to improve the cluster qualityeven though different clustering methods have been introduced, still many challenges exist for the improvement of document clustering. For web search purposea document in group is efficiently arranged for the result retrieval.The users accordingly search query in an organized way. Hierarchical clustering is attained by document clustering.To the greatest algorithms for groupingdo not concentrate on the semantic approach, hence resulting to the unsatisfactory output clustering. The involuntary approach of organizing documents of web like Google, Yahoo is often considered as a reference. A distinct method to identify the existing group of similar things in the previously organized documents and retrieves effective document classifier for new documents. In this paper the main concentration is on hierarchical clustering and k-means algorithms, hence prove that k-means and its variant are efficient than hierarchical clustering along with this by implementing greedy fast k-means algorithm (GFA) for cluster document in efficient way is considered.

Download Full-text