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A ranking is an ordering of the documents that should reflect their relevance to a user query [12].
This solution allows to avoid mapping (which may always introduce an error) terms from a user query to ontology classes.
Upon a user query, agents are spawned to mine various web sources, integrate the spatial data and return the correctly formulated query result to the user.
Typically, a user query is processed in a data center that is geographically close to the origin of the query, over a replica of the entire web index.
To overcome this shortcoming, the data-centric method further audits whether the data a user query actually accessed has involved any banned information [13, 14].
Our query-by-singing experiment indicates that a song might be retrieved based on words that are correctly recognized from a user query.
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When a user queries GM with a keyword, the keyword is encrypted by his/her group session key.
When a user queries for a signal, the server searches for the signal in the LocSigDB and displays the results.
A general workflow of how users interact with PubMed is displayed in Figure 2: a user queries PubMed or other similar systems for a particular biomedical information need.
When a user queries the database interface using a list of RefSeq accession numbers (A), RefSeq status for each identifier is retrieved (B).
To fix our terms, when a user queries a database of R records, a retrieval algorithm typically lists up to R records, ranked by some score S indicating the probability that the corresponding record has relevance to the query.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com