What Can Data Modelers Learn from Ontologists?
Norman Daoust, Daoust Associates
An ontology is the “study of … the basic categories of being and their relations” per Wikipedia. Ontologists have been working for thousands of years to understand reality. Data modelers have been working for less than fifty years to model portions of reality.
What can data modelers learn from ontologists? Have ontologists already created ontologies that encompass most of the concepts we need to include in our data models? Should we incorporate some of their ideas?
This presentation reviews two high level ontologies and examines their relevance to your data models. Attendees are encouraged to bring a data model so that you can fit it into an ontology during the exercise portion of the presentation.
Attendees will learn about two high-level ontologies, how they can significantly enhance your data models, five tips data modelers can adapt from ontologies, and what ontologies will not do for you.
Norman Daoust founded his consulting company Daoust Associates, www.DaoustAssociates.com in 2001. He is not just a data architect and data modeler: he also enjoys decision modeling, process modeling, and UML state machine modeling. His clients have included the Centers for Disease Control and Prevention (CDC), the Veteran’s Health Administration, the Canadian Institute for Health Information, several healthcare provider organizations, a Fortune 500 software company, and several start-ups. He has been an active contributor to the healthcare industry standard data model, the Health Level Seven (HL7) Reference Information Model (RIM) since its inception. Norman’s book, “UML Requirements Modeling for Business Analysts” explains how to adapt the Unified Modeling Language (UML) for analysis purposes. Norman is an engaging speaker who enjoys making complex topics easy and enjoyable.