@inproceedings{Zhou/2007/SPARK:,
title = {SPARK: Adapting Keyword Query to Semantic Search},
address = {Berlin, Heidelberg},
author = {Qi Zhou and Chong Wang and Miao Xiong and Haofen Wang and Yong Yu},
booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
month = {November},
pages = {687--700},
publisher = {Springer Verlag},
series = {LNCS},
url = {http://iswc2007.semanticweb.org/papers/687.pdf},
volume = {4825},
year = {2007},
abstract = {Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named ‘SPARK’ has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.},
keywords = {searching semanticWeb }
}
::