Communications of the Association for Information Systems


The explicit representation of context and subjectivity enables an information system to support multiple interpretations of the data it records. This is a crucial aspect of learning and innovation within scientific information systems. We present an ontology-based framework for context and subjectivity that integrates two lines of research: data provenance and ontological foundations of the Semantic Web. Data provenance provides a set of constructs for representing data history. We extend the definition of these constructs in order to describe multiple viewpoints or interpretations held within a domain. The W7 model, the Toulmin model, and the Proof Markup Language (PML) provide the Interlingua for creating multiple viewpoints of data in a machine-readable and sharable form. Example use cases in space sciences are used to demonstrate the feasibility and value of our approach.