An ontology-improved vector space model for semantic retrieval
ISSN: 0264-0473
Article publication date: 27 November 2020
Issue publication date: 12 December 2020
Abstract
Purpose
The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords.
Design/methodology/approach
In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model.
Findings
The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce.
Originality/value
This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.
Keywords
Acknowledgements
This work is funded by the National Natural Science Foundation of China (71603114), the Audit Information Engineering and Technology Collaborative Innovation Center of Jiangsu College (18CICA02) and the Jiangsu Provincial Government Scholarship Program.
Citation
Tang, M., Chen, J., Chen, H., Xu, Z., Wang, Y., Xie, M. and Lin, J. (2020), "An ontology-improved vector space model for semantic retrieval", The Electronic Library, Vol. 38 No. 5/6, pp. 919-942. https://doi.org/10.1108/EL-04-2020-0081
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited