Abstract
The knowledge embodied in cognitive models of smart environments, such as machine learning models, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labeling, network training, and fine-tuning of models. Sharing and reuse of these elaborated resources between intelligent systems of different environments, which is known as transfer learning, would facilitate the adoption of cognitive services for the users and accelerate the uptake of intelligent systems in smart building and smart city applications. Currently, machine learning processes are commonly built for intra-organization purposes and tailored towards specific use cases with the assumption of integrated model repositories and feature pools. Transferring such services and models beyond organization boundaries is a challenging task that requires human intervention to find the matching models and evaluate them. This paper investigates the potential of communication and transfer learning between smart environments in order to empower a decentralized and peer-to-peer ecosystem for seamless and automatic transfer of services and machine learning models. To this end, we explore different knowledge types in the context of smart built environments and propose a collaboration framework based on knowledge graph principles for describing the machine learning models and their corresponding dependencies.
Highlights
Today, our built environment is producing large amounts of data but—driven by the Internet of Things (IoT) paradigm—it is starting to talk back and communicate with its inhabitants and the surrounding systems and processes
IoT industries and service providers strive to find more efficient ways to benefit from the growing IoT ecosystem and combine it with other available information resources to create smarter environments equipped with cognitive models
The knowledge transfer between spaces is commonly undertaken by a human who can understand the implicit semantics of environments, features, and the relevant information resources and services
Summary
Driven by the Internet of Things (IoT) paradigm—it is starting to talk back and communicate with its inhabitants and the surrounding systems and processes. This study set out to investigate the potential of communication and reuse of cognitive knowledge between smart environments for a seamless and automatic transfer of services and machine learning models. To this end, we propose a semantic framework that lays out the description of machine learning processes and makes the corresponding digital assets explainable and interoperable. We propose a semantic framework that lays out the description of machine learning processes and makes the corresponding digital assets explainable and interoperable This is achieved by creating a knowledge graph that is aware of the IoT infrastructure of target environments, the semantics of datasets and features, and the models trained based on those features.
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