Open Access

ARTICLE

Ontology-Based Semantic Search Framework for Disparate Datasets

Paramjeet Kaur¹, Parma Nand¹, Salman Naseer², Akber Abid Gardezi³, Fawaz Alassery⁴, Habib Hamam⁵, Omar Cheikhrouhou⁶, Muhammad Shafiq^7,*
1 Auckland University of Technology, Auckland, 1010, New Zealand
2 Department of Information Technology, University of the Punjab Gujranwala Campus, Gujranwala, 52250, Pakistan
3 Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan
4 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
5 Faculty of Engineering, Moncton University, E1A3E9, Canada
6 CES Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax, 3038, Tunisia
7 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea
* Corresponding Author: Muhammad Shafiq. Email:

Intelligent Automation & Soft Computing 2022, 32(3), 1717-1728. https://doi.org/10.32604/iasc.2022.023063

Received 26 August 2021; Accepted 09 October 2021; Issue published 09 December 2021

Abstract

The public sector provides open data to create new opportunities, stimulate innovation, and implement new solutions that benefit academia and society. However, open data is usually available in large quantities and often lacks quality, accuracy, and completeness. It may be difficult to find the right data to analyze a target. There are many rich open data repositories, but they are difficult to understand and use because these data can only be used with a complex set of keyword search options, and even then, irrelevant or insufficient data may eventually be retrieved. To alleviate this situation, ontology-based semantic search has been proven to be an effective way to improve the quality of related content queries in such repositories. In this paper, we propose a new method of semantic linking and storing open government datasets of New Zealand's agriculture, land and rainfall sectors based on the use of ontology. The generated ontology can construct integrated data, in which a unified query can be applied to extract richer and more useful information. To validate our model, we showed how to link ontology manually and automatically. Manual linking requires domain experts, and automatic linking reduces the overhead of relying on domain experts to manually link concepts. The results of this method are promising in terms of improving data quality and search efficiency. In future, the proposed model can be integrated with other domain ontologies.

---

Keywords

Open data; ontology; linked open data; semantic search

---

Citations