In the Human Language Technology (HLT) domain, analytic results extracted from raw document sources are captured in varied models and formats due to the depth of what can be revealed and the diversity of interpretation. However, some common model and format must be followed to allow for multiple analytics to operate together in workflows and enable both the communication between analytics and the fusion of parallel or complementary results. This data integration problem is exacerbated when placing an emphasis on extracting knowledge from text, as the data model must be both adaptable and extensible to handle current and emerging content extraction capabilities and technologies. This paper describes a common interchange format and model designed to coordinate the extracted information from raw document sources in order to generate knowledge. The approach described adheres to the principles of adaptability and extensibility.