Abstract
This paper investigates the development of an intelligent data query framework through the use of semantic web technologies for manufacturing purposes. The primary objectives of the ontology-based data query were to develop an efficient and scalable data interoperability and retrieval system; in order to find the most relevant query results with minimum message cost, most hits per query and least response time. This document explains the idea of ontology and the application of the same in the manufacturing domain. A computer simulation software was developed based on a real case study of distributed networks of manufacturing workshops. In this research, a semantic query algorithm was developed where query results are returned by investigating the semantic richness of each workshop. Results were compared with a semantic-free search mechanism based on key performance indicators. The results show the validity of the proposed model for efficient data query when compared to random search.
Highlights
Evolution of the web from the prevailing web technologies and web services to the semantic web technologies and semantic web services is rapidly advancing
As depicted in these Figures, the ontology protocol shows a dramatic improvement of 166.67% and 7,900% in total Hit counts compared to OSQR protocol and Random protocol respectively (Himali et al, 2012)
The OSQR protocol was based on WordNet ontology which used a wide range of sample data inputs
Summary
Evolution of the web from the prevailing web technologies and web services to the semantic web technologies and semantic web services is rapidly advancing This is as a consequence of the huge amount of data available, the increasing concern of missing out on using valuable data and the cost of using wrong data for decision making (Bikakis, & Sellis, 2016; Esposito et al, 2015). It can be argued that a failure by some entities of the system to respond to some queries can be considered as a response as it provides us with some information In such an example, an absence of data provides information (informative non-response), and this is important in instances where there is minimal data. Manufacturing currently uses a mixture of data and information infrastructures such as databases of raw data collected from its internal activities and a web of information for business-to-business information transfer along the supply chain
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