Ontology is the fundamental part of Semantic Web. The goal of W3C is to bring the web into (its full potential) a semantic web with reusing previous systems and artifacts. Most legacy systems have been documented in structural analysis and structured design (SASD), especially in simple or Extended ER Diagram (ERD). Such systems need up-gradation to become the part of semantic web. In this paper, we present ERD to OWL-DL ontology transformation rules at concrete level. These rules facilitate an easy and understandable transformation from ERD to OWL. The set of rules for transformation is tested on a structured analysis and design example. The framework provides OWL ontology for semantic web fundamental. This framework helps software engineers in upgrading the structured analysis and design artifact ERD, to components of semantic web. Moreover our transformation tool, ER2OWL, reduces the cost and time for building OWL ontologies with the reuse of existing entity relationship models.
Ontology driven architecture has revolutionized the inference system by allowing interoperability and efficient reasoning between heterogeneous multivendors systems. Sound reasoning support is highly important for sound semantic web ontologies which can only be possible if state-of-the-art Description Logic Reasoners were capable enough to identify inconsistency and classify taxonomy in ontologies. We have discussed existing ontological errors and design anomalies, and provided a case study incorporating these errors. We have evaluated consistency, subsumption, and satisfiability of DL reasoners on the case study. Experiment with DL reasoners opens up number of issues that were not incorporated within their followed algorithms. Especially circulatory errors and various types of semantic inconsistency errors that may cause serious side effects need to be detected by DL reasoners for sound reasoning from ontologies. The evaluation of DL reasoners on Automobile ontology helps in updating the subsumption, satisfiability and consistency checking algorithms for OWL ontologies, especially the new constructs of OWL 1.1.
Monkeypox (MPX) is a viral zoonosis with lesions like smallpox. Though rare in Nigeria, sporadic outbreaks have been reported in 17 states since September 2017. Unfortunately, the COVID-19 pandemic has further reduced surveillance and reporting of MPX disease. This study seeks to assess the effect of an enhanced surveillance approach to detect MPX cases and measure the cumulative incidence of MPX in priority states in Nigeria. We identified three priority states (Rivers, Delta and Bayelsa) and their Local Government Areas (LGAs) based on previous disease incidence. We also identified, trained, and incentivized community volunteers to conduct active case searches over three months (January to March 2021). We supported case investigation of suspected cases and followed up on cases in addition to routine active surveillance for MPX in health facilities and communities. Weekly and monthly follow-up was carried out during the same period. Out of the three states, 30 hotspots LGAs out of the 56 LGAs (54%) were engaged for enhanced surveillance. We trained three state supervisors, 30 LGA surveillance facilitators and 600 Community informants across the three priority states. Overall, twenty-five (25) suspected cases of MPX were identified. Out of these, three (12%) were confirmed as positive. Enhanced surveillance improved reporting of MPX diseases in hotspots LGAs across the priority states. Extension of this surveillance approach alongside tailored technical support is critical intra and post-pandemic.
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