%0 Conference Paper %1 ICSC2013Expansion %A Shekarpour, Saeedeh %A Höffner, Konrad %A Lehmann, Jens %A Auer, Sören %B 7th IEEE International Conference on Semantic Computing, September 16-18, 2013, Irvine, California, USA %D 2013 %K 2013 auer event_ICSC group_aksw hoeffner lehmann lod2page shekarpour %P 191--197 %T Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features %U http://svn.aksw.org/papers/2013/ISWC2013_QueryExpansion/public.pdf %X Effective search in structured information based on textual user input is of high importance in thousands of applications. Query expansion methods augment the original query of a user with alternative query elements with similar meaning to increase the chance of retrieving appropriate resources. In this work, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Open Data. We evaluate the effectiveness of each feature individually as well as their combinations employing several machine learning approaches. The evaluation is carried out on a training dataset extracted from the QALD question answering benchmark. Furthermore, we propose an optimized linear combination of linguistic and lightweight semantic features in order to predict the usefulness of each expansion candidate. Our experimental study shows a considerable improvement in precision and recall over baseline approaches.

@inproceedings{ICSC2013Expansion, abstract = {Effective search in structured information based on textual user input is of high importance in thousands of applications. Query expansion methods augment the original query of a user with alternative query elements with similar meaning to increase the chance of retrieving appropriate resources. In this work, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Open Data. We evaluate the effectiveness of each feature individually as well as their combinations employing several machine learning approaches. The evaluation is carried out on a training dataset extracted from the QALD question answering benchmark. Furthermore, we propose an optimized linear combination of linguistic and lightweight semantic features in order to predict the usefulness of each expansion candidate. Our experimental study shows a considerable improvement in precision and recall over baseline approaches.}, added-at = {2017-01-27T23:28:47.000+0100}, author = {Shekarpour, Saeedeh and H{\"o}ffner, Konrad and Lehmann, Jens and Auer, S{\"o}ren}, bdsk-url-1 = {http://svn.aksw.org/papers/2013/ISWC2013_QueryExpansion/public.pdf}, biburl = {https://www.bibsonomy.org/bibtex/293bb5c011108991b95e387f71572b2ef/soeren}, booktitle = {7th IEEE International Conference on Semantic Computing, September 16-18, 2013, Irvine, California, USA}, ee = {http://doi.ieeecomputersociety.org/10.1109/ICSC.2013.41}, interhash = {342054d508ef295b62dfcda745cc6108}, intrahash = {93bb5c011108991b95e387f71572b2ef}, keywords = {2013 auer event_ICSC group_aksw hoeffner lehmann lod2page shekarpour}, owner = {soeren}, pages = {191--197}, timestamp = {2017-01-27T23:30:12.000+0100}, title = {Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features}, url = {http://svn.aksw.org/papers/2013/ISWC2013_QueryExpansion/public.pdf}, year = 2013 }