Automatically Prospecting Feature for Queries from Their Search Impact
Krishnaiah Nallam1, B. Ganga Bhavani2, B S N Murthy3, G. L. N. V. S. Kumar4
1Dr. Krishnaiah Nallam, Professor, B V C Engineering College, Odalarevu, East Godavari, (A.P.), India.
2B. Ganga Bhavani , Assistant Professor, B V C Engineering College, Odalarevu, East Godavari, (A.P.), India.
3B S N Murthy, Assistant Professor, B V C Engineering College, Odalarevu, East Godavari, (A.P.), India.
4G.L.N.V.S Kumar, Assistant Professor, BVC Institute of Technology and Science, East Godavari, (A.P.), India.
Manuscript received on September 12, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 180-183 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1108109119/2019©BEIESP | DOI: 10.35940/ijeat.A1108.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We recommend that you compile the duplicate lists in the top search engine results to track the aspects of the query and implement a method known as QD Miner. More specifically, QD Miner extracts free text lists, HTML tags and re regions the top search engine results, combining them with groups according to the products they contain, then line up the blocks and products, depending on how the conversation and products are included in the best results. The recommended approach is generic and does not depend on understanding any area. The main purpose of the extraction side differs from the query recommendations. We recommend a structured solution, described as QD Miner, to trace query aspects immediately by removing and grouping repetitive lists in free text results and HTML tags and repeating search engines. We continue to evaluate the support of the list and discover better search queries by looking for exact similarities between menus and penalizing duplicate lists. Experimental results reveal that there are many listings available and QD Miner can find useful queries. The proposed approach is general and does not depend on understanding a particular area. As a result, it can handle open domain queries. The query supports. Instead of a static system for your problems, we extract the sides of the uploaded document above each query.
Keywords: Mining facet, Query facet, Faceted search, re-ranking system.