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Textual web pages dominate web search engines nowadays. However, there is also a striking increase of structured data on the web. Efficient keyword query processing on structured data has attracted enough attention, but effective query understanding has yet to be investigated. In this paper, we focus on the problem of
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...
Search engines are one of the most powerful tools in the Web world today for data retrieval and exploration. Most search engines identify the key word in the sentence or phrase or list of words given by the user and starts mining the Web for the occurrence of keyword in the Web pages. Quite often searching for the key
-processing of Web search results have been extensively studied to help user effectively obtain useful information. This paper has basically three parts. First part is the review study on how the keyword is expanded through truncation or wildcards (which is a little known feature but one of the most powerful one) by using
We have become able to get enough approvable images of a target object just by submitting its object-name to a conventional keyword-based Web image search engine. However, because the search results rarely include its uncommon images, we can often get only its common images and cannot easily get exhaustive knowledge
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
plus noun phrase learning for extraction of activity concepts in Chinese. We also propose an algorithm of relevance measurement for extracting relation instances by binary keywords based on co-occurrence statistics. Finally, we build a practical system of ontology learning through learning relation instances of the
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 is growing at a rate of about a million pages per day, making it tougher for search engines to extract relevant information for its users. Earlier Search Engines used simple indexing techniques to search for keywords in websites and gave more weightage to pages with higher frequency of keyword
In this paper, we propose a method of converting a given sequence of search queries about a certain topic into a sequence of search queries about a given different topic. We define the concept of a search skeleton for topic conversion. A search skeleton represents relationships between keywords in a query. A given
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
Summary form only given. In this demonstration we present a system that guides a user's information search (or knowledge discovery) by displaying, in a coordinated manner, many valuable keywords having important semantic relations to the user's topic of interest. For example, the system contains large-scale databases
the document tags is considered as cluster name. Thus in short, web search results that are fetched from the prevailing web search engines grouped under phrases that contain one or more search keywords. This paper aims at organizing web search results into clusters facilitating quick browsing options to the browser
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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