Abstract
Supervised learning is typically challenging with insufficient amounts of labeled training data and high costs for label acquisition, creating a demand for unsupervised learning methods. In the research area of Process-Oriented Case-Based Reasoning (POCBR), this demand is created by training data that is manually-modeled and computationally-expensive labeling methods. In this paper, we propose a semi-supervised transfer learning method for learning similarities between pairs of semantic graphs in POCBR with Graph Neural Networks (GNNs). The method aims to replace the fully supervised learning procedure from previous work with an unsupervised and a supervised training phase. In the first phase, the GNNs are pretrained with a triplet learning procedure that utilizes graph augmentation and random selection to enable unsupervised training. This phase is followed by a supervised one where the pretrained model is trained on the original labeled training data. The experimental evaluation examines the quality of the semi-supervised models compared to the supervised models from previous work for three semantic graph domains with different properties. The results indicate the potential of the proposed approach for improving retrieval quality.
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Notes
- 1.
Please note that the term “adaptation” refers to its meaning in the context of transfer learning in the remainder of the paper and is not referring to the reuse phase in CBR.
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Schuler, N., Hoffmann, M., Beise, HP., Bergmann, R. (2023). Semi-supervised Similarity Learning in Process-Oriented Case-Based Reasoning. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_12
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