This paper describes an architecture for robots interacting with non-expert humans to incrementally acquire domain knowledge. Candidate questions are generated using contextual information and ranked using different measures with the objective of maximizing the potential utility of the response. We report results of some preliminary experiments evaluating the architecture in a simulated environment. Categories and Subject Descriptors I.2.6 [Learning]: Knowledge Acquisition General Terms Human Factors, Algorithms Keywords Human-robot collaboration, Knowledge Acquisition, contextual query generation