The paper addresses how a robot can maintain a state representation of all that it knows about the environment over time and space, given its observations and its domain knowledge. The advantage in combining domain knowledge and observations is that the robot can in this way project from the past into the future, and reason from observations to more general statements to help guide how it plans to act and interact. The difficulty lies in the fact that observations are typically uncertain and logical inference for completion against a knowledge base is computationally hard.

Temporal encoding schemes using RDF and OWL are often plagued by a massive proliferation of useless "container" objects. Reasoning and querying with such representations is extremely complex, expensive, and error-prone. We present a temporal extension of the Hayes and ter Horst entailment rules for RDFS/OWL. The extension is realized by extending RDF triples with further temporal arguments and requires only some lightweight forms of reasoning. The approach has been implemented in the forward chaining engine HFC.