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Keyword search over relational databases (KSORD) enables casual users to use keyword queries (a set of keywords) to search relational databases just like searching the Web, without any knowledge of the database schema or any need of writing SQL queries. In KSORD, retrieval of user's initial query is often unsatisfying
The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs. Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper
The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs.Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper
sense discovery problem. Given a query and a list of result pages, our unsupervised method detects word sense communities in the extracted keyword network. The documents are assigned to several refined word sense communities to form clusters. We use the modularity score of the discovered keyword community structure to
relevance weight between each query term and its relevant terms extracted from the snapshot of Google search result when that query term is used as search keyword. The estimated relevance weights are used to select good expansion terms for second retrieval. The experiments on the two test collections show that our query
A natural language information retrieval system ranks related documents according to criteria based on user query keywords and document similarities. However, many efforts have been made to make more useful query keywords because users do not use many keywords in their natural language search query when retrieving
Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an
associated with intermediate semantic descriptors. The intermediate descriptors are used also for image categorization and for qualitative definition of semantic keywords in the user queries. For improving the initial query results, we apply a relevance feedback mechanism that uses the low -level descriptors of the images
source, specifically Yahoo's ldquosuggested keywordsrdquo. These keywords are based on co-occurrence data across queries. The classifier, which is built offline with training data, makes use of the top-n results during training, but not duing testing. Thus, there is an asymmetry between the training and testing data. We
When searching for information a user wants, search engines often return lots of results unintended by the user. Query expansion is a promising approach to solve this problem. In the query expansion research, one of big issues is to generate appropriate keywords representing the user's intention. This paper proposes
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.