Cognonto is renowned for its planning acumen and documentation. We are experienced in all phases of budgeting, scheduling and staffing across knowledge management and knowledge representation (KM / KR) projects. We are familiar with the technologies and players that are potential contributors to specific project needs. We understand Web services, including microservices, and API designs and architectures to support multiple-vendor, multiple-source projects. We have a passion for reusable and trainable workflows and governance procedures for even the most complex of data integration and transformation projects.
Incremental build-outs are often a good strategy to let demonstrated benefits justify continuing budgets.
Cognonto principals have been the developers of scores of large-scale and prominent knowledge graphs. We have developed best practices for modular ontology design and specification. We have decades of experience in ontology mapping and integration, and the use of knowledge graphs for managing information assets in a sustainable manner.
Tieing new assets to existing computing scaffoldings, such as KBpedia, helps set the design for modularizing the domain space at hand, while providing guidance for quick conversions and ETLs. Tieing to existing knowledge bases provides both text (document entries) and attribute data for training machine learners and guiding integrations.
Knowledge representation is at the core for how to express and expose information assets. KR provides the grammar and rules by which data and information gets represented to the system (the enterprise) with vocabulary, meaning and usability. All knowledge management projects are subject to the open world assumption and must be expressed in a KR format that allows logical relationships and computability. Without these factors, a KM project quickly decays.
The success of knowledge management projects resides in basing the entire project on defensible and logically testable premises. This is not merely a matter of semantics, but also a matter of logically and empirically grounded ways for representing the base knowledge.