Informed Machine Learning for Improved Similarity Assessment in
  Process-Oriented Case-Based Reasoning
  Currently, Deep Learning (DL) components within a Case-Based Reasoning (CBR)
application often lack the comprehensive integration of available domain
knowledge. The trend within machine learning towards so-called Informed machine
learning can help to overcome this limitation. In this paper, we therefore
investigate the potential of integrating domain knowledge into Graph Neural
Networks (GNNs) that are used for similarity assessment between semantic graphs
within process-oriented CBR applications. We integrate knowledge in two ways:
First, a special data representation and processing method is used that encodes
structural knowledge about the semantic annotations of each graph node and
edge. Second, the message-passing component of the GNNs is constrained by
knowledge on legal node mappings. The evaluation examines the quality and
training time of the extended GNNs, compared to the stock models. The results
show that both extensions are capable of providing better quality, shorter
training times, or in some configurations both advantages at once.
