Query expansion is the process of adding related terms to a search query to improve the accuracy of the search. Query expansion is often used when a user’s search query does not return any results, or when the results returned are not relevant to the user’s needs.
Example:
original query: data mining
expanded query: data mining algorithms, data mining tools, data mining applications
Richer Model Query Expansion vs Discriminative Query Expansion
Discriminative query expansion is the process of adding terms to a search query that are more likely to result in relevant results. This is usually done by looking at the results of similar queries and determining which terms are most associated with relevant results.
Richer Model Query Expansion, on the other hand, is the process of adding terms to a search query that are more likely to result in results that are both relevant and diverse. This is usually done by looking at the results of similar queries and determining which terms are most associated with both relevant and diverse results.
Discriminative query expansion is often used when a user’s search query does not return any results, or when the results returned are not relevant to the user’s needs. Richer Model Query Expansion is often used when a user’s search query returns few results, or when the results returned are not diverse enough for the user’s needs.
Comparison to Other Terms
Query expansion is often compared to other terms, such as query reformulation, query modification, and query optimization. However, there are some key differences between these terms:
Query reformulation is the process of changing the wording of a search query to improve the accuracy of the search. This can be done by synonym replacement, spell correction, or other means.
Query modification is the process of adding or removing terms from a search query to improve the accuracy of the search. This can be done by stopword removal, proximity search, or other means.
Query optimization is the process of improving the performance of a search query by making it more specific, using better keywords, or by other means.