Towards open decision support systems based on semantic focused crawling

Recently, many systems, the so-called open-networked systems, have been opened to the public in a variety of ways. Such openness is providing people with not only simple services (e.g., ODBC and open API) but also their local knowledge. In this paper, we are focusing on the local knowledge which is composed of two parts: (i) domain ontology and (ii) business rules. More importantly, the local knowledge is applicable to support logical inferences of decision supporting processes in other information systems. In this context, we propose a novel framework of open decision support system (ODSS) which is capable of gathering relevant knowledge from an open-networked environment. Thereby, we exploit two main methods: (i) context-based focused crawler architecture to discover local knowledge from interlinked systems, and (ii) knowledge alignment process to integrate the discovered local knowledge. As a conclusion, we demonstrate how the merged knowledge can be exploited to support decision making efficiently by conducting some experimentations.