SignalKG: Towards Reasoning about the Underlying Causes of Sensor Observations
This work addresses the need for smarter surveillance systems that can interpret sensor data causally, though it appears incremental as it builds on existing knowledge graph concepts.
The paper tackles the problem of enabling machines to reason about the underlying causes of sensor signals, such as identifying an attacker breaking a window from surveillance data, and demonstrates a vision for knowledge graphs to achieve this.
This paper demonstrates our vision for knowledge graphs that assist machines to reason about the cause of signals observed by sensors. We show how the approach allows for constructing smarter surveillance systems that reason about the most likely cause (e.g., an attacker breaking a window) of a signal rather than acting directly on the received signal without consideration for how it was produced.