  In recent years, there has been a resurgence in methods that use distributed
(neural) representations to represent and reason about semantic knowledge for
robotics applications. However, while robots often observe previously unknown
concepts, these representations typically assume that all concepts are known a
priori, and incorporating new information requires all concepts to be learned
afresh. Our work relaxes the static assumptions of these representations to
tackle the incremental knowledge graph embedding problem by leveraging
principles of a range of continual learning methods. Through an experimental
evaluation with several knowledge graphs and embedding representations, we
provide insights about trade-offs for practitioners to match a semantics-driven
robotics application to a suitable continual knowledge graph embedding method.
