International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 2656 A Survey on Automatically Mining Facets for Queries from Their Search Results Anusree Radhakrishnan 1 Minu Lalitha Madhav 2 PG Scholar 1 , Asst. Professor 2 1 Sree Buddha College of Engineering, Alappuzha, India 2 Sree Buddha College of Engineering, Alappuzha, India Dept. of Computer Science & Engineering , Sree Buddha College of Engineering, Pattoor, Alappuzha ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Now a days we address the time consuming problem of web searching. Continuously navigating through a number of pages is a difficult task. So query facet is an optimal solution for this. Query facet can be considered as a single word / multiple words which summarize and describe that query. A query facet can be obtained by aggregating the significant lists. The query facet engine will automatically fetch the facets associated with a query. Searching will be easier with the help of facets .It also add the concept of frequent item mining. The facets are assigned a weightage value. In order to display the facets in priority wise manner utility mining concept is also integrated with it. It improves the searching Key Words: Facet, weightage, utility mining 1.INTRODUCTION Query facet is derived by analyzing the text query .It allows the users to explore collection of information by applying multiple filters. Faceted search / Faceted navigation is a technique for accessing information organized according to a faceted classification system. Query facets provide interesting and useful knowledge about a query. It improve search experiences. Query facet generate significant aspects. from a large list of queries based on a particular product/ services. Facets access a recommendation for searched users .Automatically mine query facets that exhibits the characteristics of product/ service . A query may have multiple facets that summarize information from a query from different perspectives 2 Literature Survey In [1] S. Gholamrezazadeh describes about Query-Based Summarization Query facets are a specific type of summaries that describe the main topic of given text. Existing summarization algorithms are classified into different categories in terms of their summary construction methods (abstractive or extractive),the number of sources for the summary (single document or multiple documents), types of information in the summary (indicative or informative), and the relationship between summary and query (generic or query-based). Brief introductions to them can be found. QDMiner aims to offer the possibility of finding the main points of multiple documents and thus save users’ time on reading whole documents. The difference is that most existing summarization systems dedicate themselves to generating summaries using sentences extracted from documents, while we generate summaries based on frequent lists. In addition, we return multiple groups of semantically related items, while they return a flat list of sentences. . [2] A. Herdagdelen proposes Query reformulation and query recommendation (or query suggestion) are two popular ways to help users better describe their information need. Query reformulation is the process of modifying a query that can better match a user’s information need , and query recommendation techniques generate alternative queries semantically similar to the original query. The main goal of mining facets is different from query recommendation. The former is to summarize the knowledge and information contained in the query, whereas the latter is to find a list of related or expanded queries.