Cross Organizational Data Quality and Semantic Integrity: Learning and Reasoning about Data Semantics with Context Interchange Mediation

We use a context interchange mediation approach for detecting and resolving data quality and semantic integrity conflicts in information exchanged across organizational boundaries. Context models draw on a domain ontology to explain how source and receiver data models implement general principles of the subject domain. Using the declarative knowledge from the domain ontology and context models, the mediator writes a query plan meeting receiver semantic requirements from autonomous, heterogeneous sources. Examples drawn from fixed income securities investments illustrate problems and solutions enabled by context interchange mediation.