Compliance Management (CM) is the management process that an organization implements to ensure organizational compliance with relevant requirements and expectations. Compliance Auditing (CA) is a childprocess of CM where compliance rules and policies are individually checked against the organization to determine the level of compliance achieved by the organization. In this paper, we arrange organizational knowledge and facts within OWL ontologies and model compliance rules as adaptive semantic-based rules for compliance audit automation. We study the issues of uncertainty and inconsistency in compliance and propose an adaptive human-like strategy for mimicking conventional compliance auditing.
Compliance Management (CM) is the management process that an organization implements to ensure organizational compliance with relevant requirements and expectations. It is a continual, manual and labor intensive process that is proved to be of great challenge for many organizations. CM affects almost every aspect of an organization and is in nature a complex problem due to voluminous knowledge and data involved. In our attempts to automate and simplify compliance, we propose and examine a semantic rulebased approach for modeling compliance knowledge with the use of semantic web rules (SWRL) and web ontology language (OWL). We study the use of exception handling approach to create a more robust rule base to deal with data incompleteness in the semantic web.
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