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Identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the deep learning-based approach to tackle the pill recognition problem in the literature. However, due to the high similarity between pills' appearance, misrecognition often occurs, leaving pill recognition a challenge. To this end, in this paper, we introduce a novel approach named PIKA that leverages external knowledge to enhance pill recognition accuracy. Specifically, we address a practical scenario (which we call contextual pill recognition), aiming to identify pills in a picture of a patient's pill intake. Firstly, we propose a novel method for modeling the implicit association between pills in the presence of an external data source, in this case, prescriptions. Secondly, we present a walk-based graph embedding model that transforms from the graph space to vector space and extracts condensed relational features of the pills. Thirdly, a final framework is provided that leverages both image-based visual and graph-based relational features to accomplish the pill identification task. Within this framework, the visual representation of each pill is mapped to the graph embedding space, which is then used to execute attention over the graph representation, resulting in a semantically-rich context vector that aids in the final classification. To our knowledge, this is the first study to use external prescription data to establish associations between medicines and to classify them using this aiding information. The architecture of PIKA is lightweight and has the flexibility to incorporate into any recognition backbones. The experimental results show that by leveraging the external knowledge graph, PIKA can improve the recognition accuracy from 4.8% to 34.1% in terms of F1-score, compared to baselines.