A semantic query-based approach for management decision-making

Decision-making is one of the critical activities of management in business. However, decision support systems that support management decision-making activities lack the semantics involved in responding to semantic queries involving reasoning. We consider an ontology-based knowledge base that covers linked data about organizations, people and activities in a supply chain. In this paper, we explore how to effectively answer semantic queries to support management decision-making, where custom rules are employed for answering qualitative queries. We explore the ontological data representation and a similarity measure for data integration. We present a hybrid reasoning algorithm for answering qualitative queries. This algorithm adapts the reasoner such that, when proving a goal, it does a simple retrieval when it encounters trusted items, and backward-chaining over untrusted items. We provide a case study and evaluate the hybrid reasoning algorithm on scalability, query processing time and support for management decision-making.