Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features
Document Type
Conference Paper
Publication Date
9-2013
Publication Source
IEEE Seventh International Conference on Semantic Computing
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.
ISBN/ISSN
9780769551197
Copyright
Copyright © 2013, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher
Institute of Electrical and Electronics Engineers
Peer Reviewed
yes
eCommons Citation
Shekarpour, Saeedeh; Hoffner, Konrad; Lehmann, Jens; and Auer, Sören, "Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features" (2013). Computer Science Faculty Publications. 148.
https://ecommons.udayton.edu/cps_fac_pub/148
COinS
Comments
Permission documentation on file.