Ambient computing involves several vital facets. Foremost among them are Internet of Things (IoT’s) inter-device connectivity, sensors and actuators for devices, and connections with central cloud locations. Every function of IoT, particularly the inter-device communication at the edge, requires uniform interoperability standards. Knowledge graph technology offers the best standards for meeting these demands.
A standards-based approach harmonizes all data types, making them machine-readable by describing data with semantic statements and ascribing Uniform Resource Identifiers (URIs) to each node or datum. This approach is ideal for ambient computing as it is fortified further by reasoning capabilities in which the underlying systems make intelligent inferences about their data.
Moreover, the semantic standards in knowledge graphs unify diverse business concepts, terminologies, and definitions between all data types. This facilitates the much-needed agreement about the meaning of specific data between systems. Several uniform semantic data models offer this functionality. These ontologies involve a hierarchical understanding of the specific words represented in data, resulting in uniform taxonomies.
This knowledge is necessary for devices of respective manufacturers and areas of interest to share data about an intruder in dynamically secure valuable areas. Most significantly, knowledge graphs can align data of all variations according to these standardized data models and taxonomies to connect data for optimal understanding of how even the most diverse datasets interrelate for ambient computing use cases.
Ambient computing will never become a reality until universal standards are adopted for semantic interoperability. Knowledge graphs have incorporated such standards for machine intelligence since inception. By themselves, they are uniquely positioned to make the goal of intuitive computing based on a user’s fluid interactions with their environment a reality, with the help of these standards.
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