COSMOS

Abstract

The huge diversity, big quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning. In this paper, we present COSMOS, a knowledge system that fully addresses these challenges, in an efficient way, paving the way for a new generation of knowledge systems. Using our approach, it is possible for domain experts to generate temporal knowledge rules. As those rules are saved to our knowledge base, a conflict detection mechanism detects and solves rule conflicts. Then, an inference engine is able to perform efficiently, accurate decisions, based on available factual information using reasoning and handling uncertainty. Ontologies are used to model both the factual information and the data items in the rules enabling also interoperability with existing systems. To validate our approach, as an application scenario, we deploy our infrastructure in a health environment where doctors provide rules that are activated over a patient health record. Preliminary results indicate the benefits of our approach for decision support based on health data, successfully identifying adverse events and enabling intelligent patient monitoring.