The recent spread of sensors, actuators and mobile devices, comprising the Internet of Things (IoT), provides ample opportunity to improve our quality of life through data analytics. However, as IoT data is bound by the four Vs of Big Data—volume, variety, velocity, and veracity—deriving meaningful insights becomes challenging. Today, two approaches have been employed side by side. Relying on knowledge graphs (KGs) and logical rules, knowledge-driven approaches are able to derive new high-level insights via deductive inference. By making use of semantic enrichment, they are able to improve data quality and consolidate heterogeneous data sources. Conversely, data-driven approaches process raw data by applying a wide array of machine learning techniques to capture inductive knowledge. In order to leverage the benefits of semantic enrichment to improve performance on machine learning tasks, I propose using KG embeddings as a form of semantic feature generation. However, due to their static, relational nature, current embedding techniques are not easily applicable to streams of sensory data. To this end, I propose an incremental, schema-aware embedding technique, which is updatable in an online fashion and prioritizes sensory data. Because IoT applications are often critical and machine learning approaches are usually opaque with respect to decision-making, this technique is further integrated into an interpretable, end-to-end decision model.