Sensei: Self-Supervised Sensor Name Segmentation

Sensor names as alphanumeric strings typically encode their key contextual information such as their function or physical location. We focus here on sensors used in smart building applications. In these applications, sensor names are curated in a building vendorspecific manner using different structures and esoteric vocabularies. Tremendous manual effort is needed to annotate sensor nodes for each building or even to just segment these sensor names into meaningful chunks for intelligent operation of buildings. We propose here a fully automated self-supervised framework, Sensei, that can learn to segment sensor names without any human annotation. We employ a neural language model to capture the underlying structure in sensor names and then induce self-supervision based on information from the language model to build the segmentation model. Extensive experiments on five real-world buildings comprising thousands of sensors demonstrate the superiority of Sensei over baseline methods.