# Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
## 7 Conclusions and Future Work  
This paper examines the potential of using two Siamese Graph Neural Networks (GNNs) as similarity measures for retrieving semantic graphs in Process-Oriented Case Based Reasoning (POCBR). The two presented neural networks, i.e., the Semantic Graph Embedding Model (sGEM) and the Semantic Graph Matching Network (sGMN), can be used in a similarity-based retrieval by predicting the similarities of graph pairs. Therefore, a novel encoding scheme is presented that covers the graph structure, the types of nodes and edges, and their semantic annotations. Given this encoding scheme, sGEM and sGMN are constructed by adapting two neural networks from the literature to fully process pairs of semantic graphs to predict a pairwise similarity value. Setting up the retrieval process with these neural networks in a POCBR framework and a Deep Learning (DL) framework is discussed as well. The experimental evaluation investigates how differently configured variants of sGEM and sGMN perform in similarity-based retrieval. The evaluation covers two domains with different properties, nine different similarity measures, and two different retrieval approaches. The results show the suitability of sGEM and sGMN in the evaluated scenarios. Thereby, sGEM is suitable for a MAC/FAC setup, due to its fast similarity computation and reasonable retrieval quality. Furthermore, sGMN shows great potential in approximating the ground-truth graph similarities. A focus of future research should be on optimizing the presented approach of a GNN-based retrieval. This optimization ranges from aspects of parameterization to adjustments of the data encoding scheme and the usage of different neural network structures. The neural network structures could be optimized to better process other graph domains, e.g., argument graphs [25], or even other types of complex similarity measures [56]. A structural change could be, for instance, using a differentiable ranking loss function that optimizes according to the ground-truth ordering of the retrieval results (e.g., [57]). Furthermore, the approaches from this paper should be applied to other POCBR tasks, such as case adaptation (e.g., [38]). Since adaptation is usually a knowledge-intensive process, our approaches can help by providing expressive learning capabilities combined with possibilities for domain knowledge integration. Additionally, the neural networks that are used in this work are black boxes and, thus, are not capable of explaining the results they produce. In current research (and also in the CBR community, e.g., [42]), this lack of explainability is tackled in the context of _Explainable Artificial Intelligence (XAI)_. Future research should address this issue by investigating which methods are suitable for increasing the explainability of the presented neural networks. Conceptualization, M.H. and R.B. ; methodology, M.H. ; software, M.H. ; validation, M.H. ; formal analysis, R.B. ; resources, M.H., R.B. ; data curation, M.H. ; writing--original draft preparation, M.H. ; writing--review and editing, M.H. and R.B. ; visualization, M.H. ; supervision, R.B. All authors have read and agreed to the published version of the manuscript. The publication was funded by the Open Access Fund of the University of Trier and the German Research Foundation (DFG) within the Open Access Publishing funding programme. The source code, the training data, the training parameters, the trained models, and all other supplementary resources are available online at [[https://gitlab.rlp.net/procake-embedding/procake-embedding-experiments](https://gitlab.rlp.net/procake-embedding/procake-embedding-experiments)]([https://gitlab.rlp.net/procake-embedding/procake-embedding-experiments](https://gitlab.rlp.net/procake-embedding/procake-embedding-experiments)). To set up the workspace and repeat the experiments, follow the instructions in the corresponding _ReadMe file_. Please use the code version with the tag name \"MDPL_Algorithms_Graph_Embedding_Applications\".