  Sensor deployments in Smart Homes have long reached commercial relevance for
applications such as home automation, home safety or energy consumption
awareness and reduction. Nevertheless, due to the heterogeneity of sensor
devices and gateways, data integration is still a costly and timeconsuming
process. In this paper we propose the Smart Home Crawler Framework that (1)
provides a common semantic abstraction from the underlying sensor and gateway
technologies, and (2) accelerates the integration of new devices by applying
machine learning techniques for linking discovered devices to a semantic data
model. We present a first prototype that was demonstrated at ICT 2018. The
prototype was built as a domainspecific crawling component for IoTCrawler, a
secure and privacy-preserving search engine for the Internet of Things.
