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Increasing the semantic understanding and contextual awareness of machinelearning models is important for improving robustness and reducingsusceptibility to data shifts. In this work, we leverage contextual awarenessfor the anomaly detection problem. Although graphed-based anomaly detection hasbeen widely studied, context-dependent anomaly detection is an open problem andwithout much current research. We develop a general framework for converting acontext-dependent anomaly detection problem to a link prediction problem,allowing well-established techniques from this domain to be applied. Weimplement a system based on our framework that utilizes knowledge graphembedding models and demonstrates the ability to detect outliers using contextprovided by a semantic knowledge base. We show that our method can detectcontext-dependent anomalies with a high degree of accuracy and show thatcurrent object detectors can detect enough classes to provide the neededcontext for good performance within our example domain.

Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models