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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
quality of information retrieval. The contributions of our research are twofold. First, the existing ranking algorithms of search engine are classified. And we extend expression of queries by “keyword and ”, instead of keywords only. Second, a new ranking algorithm based on user feedback and semantic tags is
database, cannot be applied to Web search. We propose a new method to support Web query refinement. Our methods is based on local analysis which clustering the search result. Unlike other clustering-base approaches, we take into consideration the distance between keywords, and guarantee no information loss. A Web search
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
This paper proposes a novel method to generate labels for grouping and organizing the search results returned by auxiliary search engines. It has applied statistical techniques to measure the quantities of co-occurrence keywords for forming the label matrix of them, and then agglomerated them into higher-level
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
decoders' outputs, an optimal hypothesis was chosen for each utterance by a topic-selection criterion minimizing an energy function with three terms: likelihood scores for the utterances; keyword co-occurrence statistics to measure the long-distance correlation; and Web-based hypothesis verification scores, which penalize
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
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
Search engines have been one of the most popular ways for people to find web pages of interest. Presently, when a user enters a keyword in a search engine, the search results are usually presented the same result to other users who search the same keyword, which might not be related to each user's field of interest
The following topics are dealt with: database technology; expert system; text-based information retrieval; keyword extraction; Web search; Web semantics; and information security management.
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
Most researches on Image Retrieval (IR) have aimed at clearing away noisy images and allowing users to search only acceptable images for a target object specified by its object-name. We have become able to get enough acceptable images of a target object just by submitting its object-name to a conventional keyword
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
taken into account when indexing documents and when performing searching. Utilizing this approach, it is possible to use a natural language to express user queries. In many cases, this way is more usual for users to describe their information needs compared to the keyword style. The factoid question answering task is one
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
This paper proposes genetic-based algorithm that uses inverted index model as a preprocessing step called GAWS. It is used as a method for finding best set of documents related to the entered user keywords. These keywords are divided into three types: main keywords, should exist keywords and should not exist keywords
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