When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.
%0 Conference Paper
%1 kraft2004
%A Kraft, R.
%A Zien, J.
%B WWW '04: Proceedings of the 13th international conference on World Wide Web
%C New York, NY, USA
%D 2004
%I ACM Press
%K informationRetrieval queryRefinement searching
%P 666--674
%R http://doi.acm.org/10.1145/988672.988763
%T Mining anchor text for query refinement
%U http://portal.acm.org/citation.cfm?id=988672.988763&coll=portal&dl=ACM&type=series&idx=SERIES968&part=series&WantType=Proceedings&title=WWW
%X When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.
%@ 1-58113-844-X
@inproceedings{kraft2004,
abstract = {When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.},
added-at = {2007-07-15T08:22:10.000+0200},
address = {New York, NY, USA},
author = {Kraft, R. and Zien, J.},
biburl = {https://www.bibsonomy.org/bibtex/290e2b86bb5c211637a123972131743f6/lysander07},
booktitle = {WWW '04: Proceedings of the 13th international conference on World Wide Web},
doi = {http://doi.acm.org/10.1145/988672.988763},
interhash = {97cd8109b0a57770d4814a2a1ab60dc6},
intrahash = {90e2b86bb5c211637a123972131743f6},
isbn = {1-58113-844-X},
keywords = {informationRetrieval queryRefinement searching},
location = {New York, NY, USA},
pages = {666--674},
publisher = {ACM Press},
timestamp = {2009-01-27T15:24:50.000+0100},
title = {Mining anchor text for query refinement},
url = {http://portal.acm.org/citation.cfm?id=988672.988763&coll=portal&dl=ACM&type=series&idx=SERIES968&part=series&WantType=Proceedings&title=WWW},
year = 2004
}