Semantic Modeling is the process of taking information and processes from multiple divergent sources and, like a human would, contextualizing that data, regardless of how it is expressed, in order to translate it to all required systems.
Consider, for example, a medical system with various billing, patient information, and database structures. Across these systems, a patient’s weight may have different labels, be expressed in different units, have different rules associated to field entry, and may be represented with different data types (such as text, a number, etc.) If the patient’s weight, which is relevant for the amount of anesthesia they should receive during an emergency surgery, has been entered several times into several different systems with different rules or structures, how is the patient’s weight reconciled? In a siloed model, healthcare providers may make a mistake by using an old weight. In a semantic model, all of these systems would stem from a singular point that understands the concept of weight regardless of how it is expressed.
Semantic models provide situational awareness, connect the dots between various stakeholders and systems, and reduce risk. Using a semantic model allows for easy translation between diverse sources and consumers. Additionally, building off of semantic models in this way reduces cost and increases interoperability for future systems.
