Sentence examples for between query and document from inspiring English sources

Similarily, text retrieval that uses dictionary-based semantic tree to weight the words between query and document is used to find semantic similarities [15].

We exploit the vector space model for generating index and query, and our new-designed KDO algorithm is utilized for providing keyword weight, so that the rank function not only considers about the relevance between query and document, but also takes into account the document quality.

The aim of this paper is to provide suitable graph-based semantics to this language, supporting both data structure variability and topological similarities between queries and document structures.

For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process.

To predict the relevance issue between queries and documents, Jiang et al. [24] and Yin et al. [25] propose a vector propagation algorithm on the click graph to learn vector representations for both queries and documents in the same term space.

After the search document space is determined relevant documents are retrieved by comparing query and document vector similarity.

(b) Matching between query and templates.

nucleotides identical between query and database sequence.

The keyword index search enables a legitimate queries to search the encrypted documents with an encrypted keyword over the encrypted indexes without revealing any information on the query and documents, even to the server.

Many information retrieval experiments have taken relevance to be a relation between a query and a document, but an arguably more accurate view is that relevance is a relation between an information need and the intellectual content represented in a document [ 34].

Our scheme fine-tunes the similarity between the query and encrypted documents by proposing the membership entropies of index words.