Abstract
Users use a few keywords to post queries to search engines. Search engines, often, fail to return answers that their users seek because the keyword queries incompletely specify the information being sought and because of the ambiguity of natural language terms. Query expansion, where additional keywords are added automatically or semi-automatically to the user's query before it is run, has been used to improve the accuracy of search engines. We propose a framework where first, we identify whether a query should be expanded based on its features. We focus on identifying queries whose results are location-sensitive and expand them using keywords from similar queries from similar locations. Similarity between queries is derived using a novel LDA-based topic-level query similarity measure. We conducted experiments with query log data from the CiteSeer digital library and see a small improvement of results due to our query expansion.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Data Mining, ICDM |
| Pages | 619-624 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE Duration: 28 Oct 2007 → 31 Oct 2007 |
Other
| Other | 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 |
|---|---|
| City | Omaha, NE |
| Period | 28/10/07 → 31/10/07 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Query expansion using topic and location. / Huang, Shu; Zhao, Qiankun; Mitra, Prasenjit; Giles, C. Lee.
Proceedings - IEEE International Conference on Data Mining, ICDM. 2007. p. 619-624 4476732.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Query expansion using topic and location
AU - Huang, Shu
AU - Zhao, Qiankun
AU - Mitra, Prasenjit
AU - Giles, C. Lee
PY - 2007
Y1 - 2007
N2 - Users use a few keywords to post queries to search engines. Search engines, often, fail to return answers that their users seek because the keyword queries incompletely specify the information being sought and because of the ambiguity of natural language terms. Query expansion, where additional keywords are added automatically or semi-automatically to the user's query before it is run, has been used to improve the accuracy of search engines. We propose a framework where first, we identify whether a query should be expanded based on its features. We focus on identifying queries whose results are location-sensitive and expand them using keywords from similar queries from similar locations. Similarity between queries is derived using a novel LDA-based topic-level query similarity measure. We conducted experiments with query log data from the CiteSeer digital library and see a small improvement of results due to our query expansion.
AB - Users use a few keywords to post queries to search engines. Search engines, often, fail to return answers that their users seek because the keyword queries incompletely specify the information being sought and because of the ambiguity of natural language terms. Query expansion, where additional keywords are added automatically or semi-automatically to the user's query before it is run, has been used to improve the accuracy of search engines. We propose a framework where first, we identify whether a query should be expanded based on its features. We focus on identifying queries whose results are location-sensitive and expand them using keywords from similar queries from similar locations. Similarity between queries is derived using a novel LDA-based topic-level query similarity measure. We conducted experiments with query log data from the CiteSeer digital library and see a small improvement of results due to our query expansion.
UR - http://www.scopus.com/inward/record.url?scp=49549113792&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49549113792&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2007.116
DO - 10.1109/ICDMW.2007.116
M3 - Conference contribution
AN - SCOPUS:49549113792
SN - 0769530192
SN - 9780769530192
SP - 619
EP - 624
BT - Proceedings - IEEE International Conference on Data Mining, ICDM
ER -