  Increasing the semantic understanding and contextual awareness of machine
learning models is important for improving robustness and reducing
susceptibility to data shifts. In this work, we leverage contextual awareness
for the anomaly detection problem. Although graphed-based anomaly detection has
been widely studied, context-dependent anomaly detection is an open problem and
without much current research. We develop a general framework for converting a
context-dependent anomaly detection problem to a link prediction problem,
allowing well-established techniques from this domain to be applied. We
implement a system based on our framework that utilizes knowledge graph
embedding models and demonstrates the ability to detect outliers using context
provided by a semantic knowledge base. We show that our method can detect
context-dependent anomalies with a high degree of accuracy and show that
current object detectors can detect enough classes to provide the needed
context for good performance within our example domain.
