Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding.[1] In the context of search engines, query expansion involves evaluating a user's input (what words were typed into the search query area, and sometimes other types of data) and expanding the search query to match additional documents. Query expansion involves techniques such as:

Query expansion is a methodology studied in the field of computer science, particularly within the realm of natural language processing and information retrieval.

Precision and recall trade-offs

Search engines invoke query expansion to increase the quality of user search results. It is assumed that users do not always formulate search queries using the best terms. Best in this case may be because the database does not contain the user entered terms.

By stemming a user-entered term, more documents are matched, as the alternate word forms for a user entered term are matched as well, increasing the total recall. This comes at the expense of reducing the precision. By expanding a search query to search for the synonyms of a user entered term, the recall is also increased at the expense of precision. This is due to the nature of the equation of how precision is calculated, in that a larger recall implicitly causes a decrease in precision, given that factors of recall are part of the denominator. It is also inferred that a larger recall negatively impacts overall search result quality, given that many users do not want more results to comb through, regardless of the precision.

The goal of query expansion in this regard is by increasing recall, precision can potentially increase (rather than decrease as mathematically equated), by including in the result set pages which are more relevant (of higher quality), or at least equally relevant. Pages which would not be included in the result set, which have the potential to be more relevant to the user's desired query, are included, and without query expansion would not have, regardless of relevance. At the same time, many of the current commercial search engines use word frequency (tf-idf) to assist in ranking.[citation needed] By ranking the occurrences of both the user entered words and synonyms and alternate morphological forms, documents with a higher density (high frequency and close proximity) tend to migrate higher up in the search results, leading to a higher quality of the search results near the top of the results, despite the larger recall.

Query expansion methods

Automatic methods for query expansion were proposed in 1960 by Maron and Kuhns.[2] Modern query expansion methods either imply document collection analysis (global or local) [3] or are dictionary- or ontology-based.[4] The global analysis of the document collection is applied for searching for relations between terms. The local analysis refers to the relevance feedback introduced by Rocchio.[5] Rocchio proposed to judge manually some of the retrieved documents and use this feedback information to expand the query. Since collecting users' judgment can be challenging, only the first top retrieved documents are considered as relevant. This is so called pseudo-relevance feedback (PRF).[6] Pseudo-relevance feedback is efficient in average but can damage results for some queries,[7] especially difficult ones since the top retrieved documents are probably non-relevant. Pseudo-relevant documents are used to find expansion candidate terms that co-occur with many query terms.[8] This idea was further developed within the relevance language model formalism in positional relevance [9] and proximity relevance models [10] which consider the distance to query terms in the pseudo-relevant documents. Another direction in query expansion is the application of word embeddings.[11]

An alternative to query expansion is document expansion, which reformulates the text of the documents being searched rather than the text of the query.[12]

See also

Software libraries

References

Citations

  1. ^ Vectomova, Olga; Wang, Ying (2006). "A study of the effect of term proximity on query expansion". Journal of Information Science. 32 (4): 324–333. CiteSeerX 10.1.1.552.5987. doi:10.1177/0165551506065787. S2CID 7265523.
  2. ^ Maron, M. E. and Kuhns, J. L. 1960. On Relevance, Probabilistic Indexing and Information Retrieval. Journal of the ACM 7, 3, 216–244.
  3. ^ C. Carpineto and G. Romano. A survey of automatic query expansion in information retrieval. ACM Computing Surveys, 44(1):1-50, Jan. 2012.
  4. ^ J. Bhogal, A. Macfarlane, and P. Smith. A review of ontology based query expansion. Inf. Process. Manage., 43(4):866-886, July 2007.
  5. ^ J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System, p. 313-323. 1971.
  6. ^ C. Buckley. Automatic query expansion using SMART: TREC 3. In Proceedings of The third Text REtrieval Conference (TREC-3). NIST Special Publication, p. 69-80. National Institute of Standards and Technology, 1995.
  7. ^ G. Amati, C. Carpineto, and G. Romano. Query difficulty, robustness, and selective application of query expansion. Advances in Information Retrieval, p. 127-137, 2004.
  8. ^ J. Xu and W. B. Croft. Query expansion using local and global document analysis. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pages 4-11. ACM, 1996.
  9. ^ Y. Lv and C. Zhai. Positional relevance model for pseudo-relevance feedback. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, page 579-586. ACM, 2010.
  10. ^ L. Ermakova, J. Mothe, and E. Nikitina. 2016. Proximity relevance model for query expansion. In Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC '16). ACM, New York, NY, USA, 1054-1059. DOI: https://doi.org/10.1145/2851613.2851696
  11. ^ S. Kuzi, A. Shtok, and O. Kurland. 2016. Query Expansion Using Word Embeddings. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM '16). ACM, New York, NY, USA, 1929-1932. DOI: https://doi.org/10.1145/2983323.2983876
  12. ^ Lin, Jimmy; Nogueira, Rodrigo; Yates, Andrew (2020-10-13). "Pretrained Transformers for Text Ranking: BERT and Beyond". arXiv:2010.06467 [cs.IR].
  13. ^ Mahtab Tamannaee, Hossein Fani, Fattane Zarrinkalam, Jamil Samouh, Samad Paydar, Ebrahim Bagheri: ReQue: A Configurable Workflow and Dataset Collection for Query Refinement. CIKM 2020: 3165-3172
  14. ^ Hossein Fani, Mahtab Tamannaee, Fattane Zarrinkalam, Jamil Samouh, Samad Paydar, Ebrahim Bagheri; An Extensible Toolkit of Query Refinement Methods and Gold Standard Dataset Generation. In Advances in Information Retrieval: 43rd European Conference on IR Research (ECIR'21), 2021.

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