Title

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

Comments

Permission documentation on file.

Publisher

Institute of Electrical and Electronics Engineers

Peer Reviewed

yes


Share

COinS