Representing, Querying, and Visualizing Health-Insurance Knowledge in a Cost-Sharing Estimator

Cost-Sharing Estimator (CostShEs) 1.0 is a prototype for health-insurance knowledge representation, querying, and visualization. CostShEs 1.0 allows client-side consultation about the cost of a healthcare service for a client with/without insurance. The input to this prototype system is a form-based inquiry by a client about the cost of a service. Based on the client’s entered membership number, desired service, etc., CostShEs 1.0 generates a logical query. The output is the calculated cost of the service, optionally as a text or a visualization. For logical reasoning, CostShEs 1.0 requires (1) an RDFS taxonomy, (2) webized Rule Markup Language (RuleML) data, and (3) RuleML decision rules. The taxonomy helps the system to find the client’s relatives that can take advantage of his/her insurance. The webized RuleML data are relational ground facts that link to the relevant information on web pages, such as a client’s web page. Based on (1) and (2), the decision rules answer the query. These three components form the knowledge base that CostShEs uses for reasoning with the Object Oriented Java Deductive Reasoning Engine for the Web (OO jDREW). The visualization of the system’s output in Graph Inscribed Logic (Grailog) is obtained via GrailogKSViz 2.0 translation of RuleML/XML to Scalable Vector Graphics (SVG/XML).