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.
Highlights
The main goal of the World Wide Web (WWW) has always been to allow people to access information, regardless of whether machines use the web network to transmit information
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
We propose a new method of using ontology semantics to link and store open government datasets of New Zealand's agriculture, land, and rainfall sectors
Summary
The main goal of the World Wide Web (WWW) has always been to allow people to access information, regardless of whether machines use the web network to transmit information. Portals and data on the website are only suitable for keyword-based searches, where the keywords entered by the user match the available data descriptions [6]. For a better search, the same alternative words may appear, but the user can't know these terms because the user may not be familiar with the structure used by the data publisher to describe it. There may be synonyms that match the user's intent, but the user does not know the actual terminology the publisher uses to refer to their repository. Semantic search solves this problem and aims to improve the accuracy of the search by considering the searcher's intention and the contextual importance of the search term [7]. The motivation of our research is to explore whether semantic technology can improve the usability and efficiency of search in more and more open data
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