Abstract
With the current trends and developments in the information technology domain, there is a high enthusiasm for using semantic Web technologies and decision analysis mechanisms to solve numerous recurring issues in societies. There are plenty of existing knowledge models available on the Internet, developed for solving various problems. But, the reusability aspects of those are almost very low, due to main barriers, such as complexities associated with schema understanding, technical barriers associated with querying and comprehension of semantic representations. This will hinder the reusability of existing knowledge models and also knowledge dissemination associated with new and existing knowledge models. These consequences are obstructing the opportunities of experiencing the advancements of semantic technologies to both technical and non-technical audiences. This research is focusing on proposing an architectural structure leading towards a framework, to resolve most of the above-listed technical barriers and open doors to wider audiences in experiencing the benefits of the semantic Web. The proposed architectural structure is a combination of an instructional upper ontology and multiples of decision support systems integrated to the endpoints of the upper ontology. Crime domain is selected for the proposal of the high-level architectural design, leading towards a framework, as crime escalation has been a crucial concern which needs timely attention to under control the further spread.
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Vidanage, K., Noor, N.M.M., Mohemad, R., Bakar, Z.A. (2020). Semantic Web-Based Knowledge Extraction: Upper Ontology Guided Crime Knowledge Discovery. In: Vasudevan, H., Michalas, A., Shekokar, N., Narvekar, M. (eds) Advanced Computing Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3242-9_30
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