query expansion


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query expansion

(information science)
Adding search terms to a user's search. Query expansion is the process of a search engine adding search terms to a user's weighted search. The intent is to improve precision and/or recall. The additional terms may be taken from a thesaurus. For example a search for "car" may be expanded to: car cars auto autos automobile automobiles.

The additional terms may also be taken from documents that the user has specified as being relevant; this is the basis for the "more like this" feature of some search engines.

The extra terms can have positive or negative weights.
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Query expansion is a classic technique to reformulate the query, which generates diversified expansion terms to enhance the original query.
Semantic Query Expansion. According to the existing literature, there are two types of semantic query expansion methods: those based on semantic relation/structure and those based on large-scale corpus [30, 31].
The determination of the user's need/intent forms the basis for formulating a search plan that specifies the collection(s) to be searched, the search algorithm to use, and the query transformed in many ways: marking off phrases, disambiguating query terms, query expansion, and more).
geometric consistency checking [16][30], query expansion [8][7][1] also further significantly improve the performance of the searching system.
They built a corpus of slide-paper pairs and used four presentations from it to evaluate four aligners which utilize methods such as TF-IDF term weighting and query expansion. The query expansion does not improve performance in our application and that TF-IDF term weighting is inferior to a much simpler scoring mechanism based on the number of matched terms.TF-IDF term weighting is inferior to a simpler scoring mechanism based only on the number of matched terms and query expansion degrades aligner performance.
A thesaurus structure used in query expansion ("SOW" being mapped as equivalent to the term "proposal" would allow someone searching on either term to return documents tagged with or containing the other) deals with situations where multiple terms can mean the same thing.
SSDCN approach is compared with keyword based search, WordNet-based lexical query expansion and ontology-based semantic query expansion.
In query expansion, related words are added to user's original query and form a longer and more precise query to express users' retrieval intentions.
It is charged with "defining standards and/or best practices for the new generation of library discovery services that are based on indexed search." This is decidedly less sexy than article recommenders, rich snippets of information to provide content to searchers, automatic query expansion, or any of the other "beyond the single search box" features I'll be discussing here.
Using query expansion (QE) techniques, a query is reformulated to improve retrieval performance and obtain additional relevant documents by expanding the original query with additional relevant terms and reweighting the terms in the expanded query.
The specific topics include query expansion based on Mongolian semantics, subspace culling for continuous collision detection, designing and implementing a static test approach for embedded software, an area-based image matching algorithm and its implementation, and the real-time estimation of the road friction coefficient for an electric vehicle.