Scaling Knowledge Graphs for Automating AI of Digital Twins
This work addresses scaling issues for automating AI in Digital Twins, which is incremental as it builds on existing semantic models and knowledge graph technologies.
The paper tackles the challenge of scaling knowledge graphs to automate AI pipelines for Digital Twins in IoT systems, proposing a reference architecture used in IBM products and deriving lessons learned from practical use cases.
Digital Twins are digital representations of systems in the Internet of Things (IoT) that are often based on AI models that are trained on data from those systems. Semantic models are used increasingly to link these datasets from different stages of the IoT systems life-cycle together and to automatically configure the AI modelling pipelines. This combination of semantic models with AI pipelines running on external datasets raises unique challenges particular if rolled out at scale. Within this paper we will discuss the unique requirements of applying semantic graphs to automate Digital Twins in different practical use cases. We will introduce the benchmark dataset DTBM that reflects these characteristics and look into the scaling challenges of different knowledge graph technologies. Based on these insights we will propose a reference architecture that is in-use in multiple products in IBM and derive lessons learned for scaling knowledge graphs for configuring AI models for Digital Twins.