1. Openness: The Knowledge Sharing Core Charter requires that all embedded technology be openly disclosed to the public. The common disclosure of patents, patent pending and trade secretes allows scientists and consumers to drive improvements through open collaboration. Good design and good science accelerates program innovation while reducing cost and eliminating dependency on grants.
2. Infrastructure unencumbered by technology secretes: We develop a software infrastructure more conducive to ontology operations. Conventional software infrastructure is optimized for retention of proprietary positions, while the Knowledge Sharing Core is optimized to support human knowledge sharing.
3. Multiple methods produce evolution: Simultaneous use of radically different algorithmic processes will provide for cross validation and measurement of outcomes. As in nature, variety and selection are used to drive evolution of domain specific knowledge extraction systems.
4. Science Committee: A committee of leading scholars will provide a review of the theory and practice as realized in Core processes and activities. Interoperability with OWL ontology and with Topic Maps will be built in. Specific scholars will be invited to serve on our Science Oversight Committee. Our team includes one of the authors of the Topic Maps 1.0 standard. Steven Newcomb’s role will be as a knowledgeable interface with the Virginia Bioinformatics Institute. John Sowa, leading scholar on Cognitive Graphs, will advise the team.
5. Human component: The project takes into account the human component in a human/machine reasoning system. Most conventional approaches attempt to create an autonomous reasoner with only supervisory participation by humans. Core architecture develops a patented data encoding and already deployed, as intellectual technology that directly supports various types of human memory and anticipation.
6. New form of mathematics: Computational processes produce a natural organizational stratification and mathematical convolutions over localized bits of information, in the form of (type:value) pairs residing in the computer.
7. Differential and Formative Ontology: We create several advances in ontology processing, including data reduction using categorical abstraction into localized containers. Containers are created in those places where ambiguation or disambiguation processes are essential. The contents of these containers are sets of (type:value) pairs that are double encoded into hash tables to provide almost instantaneous set theoretic operations, including support for convolutions based on a type differentiating template.
8. Inferential support and contextualization: Traditional foundations of mathematics and logic are extended and used to supply inferential support for various processes, including automation of ambiguation and disambiguation during natural language parsing as part of ontology construction.
9. Applied memetic research over active text literatures: Basic research produces a situational rendering from latent semantic analysis (patented by SAIC) and other techniques (patents supporting NdCore, Semio) revealing linguistic variations that can be used to track memetic expression in social discourse and medical research literatures.