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.
%0 Book Section
%1 citeulike:4039817
%A Zhou, Qi
%A Wang, Chong
%A Xiong, Miao
%A Wang, Haofen
%A Yu, Yong
%D 2008
%J The Semantic Web
%K aswc07, iswc07
%P 694--707
%R http://dx.doi.org/10.1007/978-3-540-76298-0\_50
%T SPARK: Adapting Keyword Query to Semantic Search
%U http://dx.doi.org/10.1007/978-3-540-76298-0\_50
%X 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.
@incollection{citeulike:4039817,
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.},
added-at = {2009-02-13T13:22:04.000+0100},
author = {Zhou, Qi and Wang, Chong and Xiong, Miao and Wang, Haofen and Yu, Yong},
biburl = {https://www.bibsonomy.org/bibtex/2ac63bc9707e9a17d07198faded492cfb/conchuir},
citeulike-article-id = {4039817},
doi = {http://dx.doi.org/10.1007/978-3-540-76298-0\_50},
interhash = {c68668cf40bf7fe01c5a6fa03b1d529a},
intrahash = {ac63bc9707e9a17d07198faded492cfb},
journal = {The Semantic Web},
keywords = {aswc07, iswc07},
pages = {694--707},
posted-at = {2009-02-12 18:11:52},
priority = {2},
timestamp = {2009-02-13T13:22:05.000+0100},
title = {SPARK: Adapting Keyword Query to Semantic Search},
url = {http://dx.doi.org/10.1007/978-3-540-76298-0\_50},
year = 2008
}