In Hybrid AI, machine learning and knowledge engineering are combined to have the best of both worlds. Insights obtained from data are combined with complementary expert knowledge, which can be represented in a graph structure. Graph networks are a recently new development in machine learning and cover methods that learn on graph structured data. This paper researches how knowledge can be incorporated in graph networks for the use case of scene classification. The aim is to detect novel scenes, of which only a few examples and noisy object detections are available. The results show that both using a graph network and adding knowledge can improve performance, however, this is not always necessarily the case. The novelty of this paper is threefold: 1. Using GNNs for scene classification; 2. Combining data and knowledge in GNNs by constructing one input graph; 3. Using GNNs in cases with few training samples and noisy inputs.
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