Semantic technologies allow the interpretation of data streams into meaningful operational semantic knowledge.
An integral part of the semantic knowledge discovery is a semantic model that represents a set of rules, patterns and templates used by a semantic system for semantic inference.
The capacity of a semantic system’s inference capabilities may increase as the semantic model evolves through semantic inference, modeling and learning.
A semantic field represents the potential of semantic knowledge discovery for a semantic system through direct sensing and inference.
By using sensing or inference artifacts to derive semantics a system achieves a particular semantic coverage which represents the actual system capabilities for semantic knowledge generation. Hence, the semantic coverage can be expanded by adding new sensing or inference artifacts to the operational semantic capabilities of the system.
Semantic collaboration means that disparate systems can work together in achieving larger operational capabilities while enhancing the semantic coverage far beyond one’s system semantic field.