Informed Machine Learning for Improved Similarity Assessment in Process-Oriented Case-Based Reasoning

TL;DR

This paper explores integrating domain knowledge into Graph Neural Networks to enhance similarity assessment in process-oriented Case-Based Reasoning, resulting in improved accuracy and training efficiency.

Contribution

It introduces methods to embed domain knowledge into GNNs for CBR, combining structural encoding and constrained message passing to improve performance.

Findings

Enhanced similarity assessment accuracy

Reduced training times for GNNs

Effective integration of domain knowledge

Abstract

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.