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An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites. These pages are often referred to as the hidden Web or the deep Web. Since there are no static links
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
Keyword auctions are being used to sell the positions along the side of organic results shown by search engine when user types a keyword or a query related to keyword in a search engine. It has been a huge revenue generating arena for search engines since last decade. Irrespective of the great success of these types
Peer-to-peer approaches bring one perfect alternative for the Web content search. However, how to search and retrieve the data based on the content query is still an open problem for peer-to-peer systems. In this paper we propose History-based Multi-keywords Search(HMS) in unstructured peer-to-peer systems, which only
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
With the Web of today being unstructured and semantically heterogeneous, keyword-based queries are likely to miss important results. Therefore, refining and expanding queries plays an important role today. This paper presents a novel approach for query expansion that applies dependency rules mined from a large Web
TASTIER is a research project on the new information-access paradigm called type-ahead search, in which systems find answers to a keyword query on-the-fly as users type in the query. In this paper we study how to support fuzzy type-ahead search in TASTIER. Supporting fuzzy search is important when users have limited
With the development of Internet, more and more on-line information has become precious wealth that we can access to. High quality information is often stored in dedicated digital libraries. However, query system of most digital libraries based on keyword matching couldnpsilat make users satisfied. This paper presents
multilingual information where backend will be English database and front-end uses local languages like Hindi, Marathi or Gujrathi. Our system provides an interface to enter a keyword in local language, the keyword will be parsed, query will be formed and display the result in local language. We had developed an efficient
The explosive growth of content makes it difficult for end-users to find data that they want. Keyword-based searches are brittle - they require the user to know the set of keywords that the system is using, and are ineffective for finding data based on meaning. This paper describes a new hierarchical overlay model
This system proposes Indian-logic ontology based Context-aware Query Refinement model to support context-sensitive semantic search in keyword based search engine. This is by formulating effective query using Indian logic based Ontology for Context identification to overcome ambiguous query terms and increase the
Lack of overall ecological knowledge structure is a critical reason for learners' failure in keyword-based search. To address this issue, this paper firstly presents the dynamic location-aware and semantic hierarchy (DLASH) designed for the learners to browse images, which aims to identify learners' current
With the development of internet, web information increases fast, how to filter information which users wanted quickly and accurately is becoming a big problem. But the traditional keyword based search system's recall rate and precision are yet to be improved. Kam-so, the user interesting collaborative filtering model
With the fast growth of the Web, users often suffer from the problem of information overload since many existing search engines response lots of non-relevant documents containing query terms based on the search mechanism of keyword matching. In fact, it is eagerly expected by both users and search engine developers to
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
structure and web content strategies, uses hyperlink as genetic individual and topic-keywords based VSM is used to evaluate individual fitness, and imports new URLs to implement crossover and mutation, and the URLs that have the same prefix are regarded as niche. Guide the crawl direction by niche genetic algorithm to
Applying automatic summarization to search engine can make it easier for users to find out the content of the Web page. In this paper, the results of search engine are analyzed. On the basis of query keywords expansion, we propose a new summary approach which calculates the sentence weight utilizing the information of
We tackle problems related to Web query formulation: given the set of keywords from a search session, 1) we find a maximum promising Web query, and, 2) we construct a family of promising Web queries covering all keywords. A query is promising if it fulfills user-defined constraints on the number of returned hits. We
As an approach to search /retrieve such objects as pictures, music, perfumes and apparels on the Internet, sensitivity-vectors or kansei-vectors are useful since textual keywords are not sufficient to find objects that users want. The sensitivity-vector is an array of values each indicates a degree of feeling or
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.