Various embodiments may include a machine-readable medium, computing device, and/or computer-implemented method for deriving inferences associated with semantic concepts using machine learning. In one embodiment, a machine learning model is trained to derive inferences associated with semantic concepts using a distributed knowledge graph (DKG) data structure. The DKG data structure represents semantic concepts from a training dataset as a set of vectors in a vector space, wherein elements of the vectors correspond to meta-semantic parameters associated with the semantic concepts. The meta-semantic parameters include: a temporal parameter to represent timestamps associated with the semantic concepts; and a spatial parameter to represent physical locations associated with the semantic concepts. An input vector with elements corresponding to the meta-semantic parameters is obtained based on data captured by sensor(s). An inference associated with semantic concept(s) corresponding to the input vector is then derived based on the machine learning model.
MACHINE LEARNING USING SEMANTIC CONCEPTS REPRESENTED WITH TEMPORAL AND SPATIAL DATA