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Given a set of keywords, we find a maximum Web query (containing the most keywords possible) that respects user-defined bounds on the number of returned hits. We assume a real-world setting where the user is not given direct access to a Web search engine's index, i.e., querying is possible only through an interface
Query-recommendation systems based on inputted queries have become widespread. These services are effective if users cannot input relevant queries. However, the conventional systems do not take into consideration the relevance between recommended queries. This paper proposes a method of obtaining related queries and clustering them by using the history of query frequencies in query logs. We define...
FCA, a session interest concept is defined as a pair of extent and intent where the extent covers a set of documents selected by the user among the search results and the intent covers a set of keyword features extracted from the selected documents. And, in order to make a concept network grow, we need to calculate the
In this paper, we propose the ldquoaddedrdquo use of proximity search to a Web search query for narrowing down the set of documents returned as answers to a keyword based search query. This approach adds value to Web search query results by allowing users to better express what they are looking for. Most of the
A simple search keyword usually returns million of search results. The result count may appear impressive, at the same time it confuse the users. User usually will not wish to browse through million of entries. This paper proposed a query refinement method by iterative clustering of information from the Web page
Given a user keyword query, current Web search engines return a list of individual Web pages ranked by their "goodness" with respect to the query. Thus, the basic unit for search and retrieval is an individual page, even though information on a topic is often spread across multiple pages. This degrades the quality of
The World Wide Web contains vast amount of interlinked web documents. Retrieving information from such a huge collection is easy using various search engines, but retrieving relevant information is still a challenging task. Since the traditional search engines are based upon keyword matching, therefore semantics of
some problems, they tend to retrieve the information from the Web search engines. Many business search engines are efficient at identifying the best web sites for any given keyword query. Unfortunately, the information on the web is not always correct. Moreover, different web sites often provide different information on a
Nowadays, Internet users are familiar with the Web searching process; and searching is the most common task performed on the Web. However, the web search is especially difficult for beginners when they try to utilize a keyword query language. Subsequently, beginners usually try to find information with ambiguous
weight of every sentence in a topic block is calculated in view of query keywords. Finally, several important sentences are dynamically extracted to compose the digest according to expected compression ratio with the improved maximal marginal relevance method, which can remove redundant information in summary. The
, current search engines do not allow users to explore these features when posing a query. Search engine queries are based almost exclusively on keywords. We believe that it is possible to improve user satisfaction if HTML tags and document metadata are available to users at query time. In this paper we present Xearch, a meta
Previous studies reveal that half of the queries submitted to search engines have no follow-up click-through data. This may indicate that users are either dissatisfied with the performance of current search engines or have difficulty formulating correct query keywords related to their search intents. To address this
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