Adapting Knowledge Graphs to Edge Computing Devices.

The emergence of increasingly powerful and inexpensive single-board computers has motivated a great deal of work in the field of edge computing. We believe that knowledge graphs will contribute to intelligent edge computing. This requires the ability to efficiently answer queries requiring inferences performed with minimal knowledge accessible on a device at the edge of the network. In this work, we determine the minimum size of the knowledge graph that an edge device needs based on the analysis of its query workload. In the context of a succinct data structures-based store, we also present an incremental update of this knowledge graph when new queries are introduced into the environment. We demonstrate the effectiveness of our solution on real use cases encountered by our industrial partner.