  The human mind is a powerful multifunctional knowledge storage and management
system that performs generalization, type inference, anomaly detection,
stereotyping, and other tasks. A dynamic KR system that appropriately profiles
over sparse inputs to provide complete expectations for unknown facets can help
with all these tasks. In this paper, we introduce the task of profiling,
inspired by theories and findings in social psychology about the potential of
profiles for reasoning and information processing. We describe two generic
state-of-the-art neural architectures that can be easily instantiated as
profiling machines to generate expectations and applied to any kind of
knowledge to fill gaps. We evaluate these methods against Wikidata and crowd
expectations, and compare the results to gain insight in the nature of
knowledge captured by various profiling methods. We make all code and data
available to facilitate future research.
