Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
Abstract
:1. Introduction
- a comprehensive encoding scheme that enables the integration of semantic graphs with their semantic annotations and structure to be used in GNNs;
- two specialized, adapted GNN architectures for learning similarities between semantic graphs, based on GNNs from the literature [23];
- an evaluation of the GNNs in different retrieval scenarios with regard to performance and quality.
2. Foundations and Related Work
2.1. Semantic Graph Representation
2.2. Similarity Assessment of Semantic Graphs
2.3. Similarity-Based Retrieval of Semantic Graphs
2.4. Related Work
3. Neural Networks for Graph Embedding
3.1. General Neural Network Structure
3.2. Embedder
3.3. Propagation Layer
3.4. Aggregator and Graph Similarity
4. Neural-Network-Based Semantic Graph Similarity Measure
4.1. Encoding Semantic Graphs
4.1.1. Encoding Node and Edge Types
4.1.2. Encoding Semantic Descriptions
4.2. Adapted Neural Network Structure
4.2.1. Adapted Embedder
4.2.2. Constrained Propagation in the Semantic Graph Matching Network
4.2.3. Trainable Graph Similarity of Semantic Graph Matching Network
4.2.4. Training and Optimization
5. Application of Semantic Graph Embedding Model and Semantic Graph Matching Network in Similarity-Based Retrieval
5.1. Offline Training
5.2. Retrieval
6. Experimental Evaluation
sGEM | sGEM used with sequence encoding; |
sGEMtree | sGEM used with tree encoding; |
sGMN | sGMN used with sequence encoding; |
sGMNtree | sGMN used with tree encoding; |
sGMNconst | sGMN used with matching constraints; |
sGMNtree,const | sGMN used with tree encoding and matching constraints. |
6.1. Experimental Setup
6.2. Experimental Results
6.3. Discussion
7. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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sGEM | sGEMtree | sGMN | sGMNtree | sGMNconst | sGMNtree,const | FBM | EBM | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fs | k | Quality | Time | Quality | Time | Quality | Time | Quality | Time | Quality | Time | Quality | Time | Quality | Time | Quality | Time | |
CB-I | 5 | 5 | 0.508 | 16 | 0.511 | 14 | 0.489 | 1051 | 0.490 | 2067 | 0.489 | 1074 | 0.489 | 2169 | 0.557 | 510 | 0.499 | 17 |
50 | 5 | 0.613 | 100 | 0.600 | 95 | 0.522 | 1137 | 0.523 | 2156 | 0.511 | 1165 | 0.505 | 2263 | 0.836 | 611 | 0.562 | 100 | |
10 | 10 | 0.520 | 24 | 0.536 | 22 | 0.502 | 1061 | 0.503 | 2077 | 0.500 | 1085 | 0.505 | 2180 | 0.585 | 522 | 0.516 | 25 | |
80 | 10 | 0.623 | 155 | 0.609 | 151 | 0.581 | 1195 | 0.576 | 2225 | 0.548 | 1224 | 0.575 | 2330 | 0.862 | 671 | 0.613 | 158 | |
25 | 25 | 0.545 | 50 | 0.567 | 48 | 0.534 | 1088 | 0.536 | 2106 | 0.518 | 1113 | 0.534 | 2208 | 0.652 | 554 | 0.549 | 53 | |
100 | 25 | 0.606 | 192 | 0.593 | 187 | 0.646 | 1240 | 0.678 | 2268 | 0.607 | 1266 | 0.676 | 2373 | 0.833 | 714 | 0.642 | 195 | |
CB-II | 5 | 5 | 0.392 | 66 | 0.394 | 84 | 0.381 | 2811 | 0.449 | 3539 | 0.399 | 2729 | 0.463 | 3666 | 0.646 | 457 | 0.329 | 57 |
50 | 5 | 0.557 | 866 | 0.563 | 895 | 0.629 | 3150 | 0.757 | 3833 | 0.671 | 3016 | 0.767 | 3928 | 0.922 | 619 | 0.385 | 295 | |
10 | 10 | 0.432 | 113 | 0.448 | 123 | 0.467 | 2825 | 0.516 | 3572 | 0.457 | 2785 | 0.508 | 3690 | 0.667 | 477 | 0.362 | 84 | |
80 | 10 | 0.625 | 1146 | 0.637 | 1158 | 0.745 | 3406 | 0.837 | 4064 | 0.749 | 3372 | 0.853 | 4197 | 0.939 | 744 | 0.444 | 430 | |
25 | 25 | 0.506 | 281 | 0.511 | 628 | 0.550 | 3004 | 0.623 | 3683 | 0.547 | 2856 | 0.628 | 3793 | 0.694 | 533 | 0.416 | 198 | |
100 | 25 | 0.683 | 1371 | 0.697 | 1303 | 0.799 | 3597 | 0.872 | 4234 | 0.797 | 3537 | 0.881 | 4320 | 0.907 | 866 | 0.500 | 518 |
Domain | CB-I | CB-II | ||||
---|---|---|---|---|---|---|
Retriever | MAE | Correctness | Time | MAE | Correctness | Time |
sGEM | 0.158 | 0.017 | 1.3 | 0.337 | 0.331 | 1.1 |
sGEMtree | 0.219 | 0.053 | 0.9 | 0.323 | 0.310 | 0.9 |
sGMN | 0.033 | 0.287 | 1025.5 | 0.034 | 0.583 | 2697.8 |
sGMNtree | 0.039 | 0.322 | 2115.1 | 0.026 | 0.724 | 3508.1 |
sGMNconst | 0.029 | 0.330 | 1051.9 | 0.033 | 0.583 | 2643.7 |
sGMNtree,const | 0.037 | 0.327 | 2118.3 | 0.024 | 0.732 | 3403.8 |
FBM | 0.193 | 0.598 | 482.7 | 0.199 | 0.584 | 335.4 |
EBM | 0.380 | 0.224 | 1.2 | 0.397 | 0.006 | 1.1 |
A*M | 0.062 | 0.669 | 1265.5 | 0.041 | 0.824 | 3801.8 |
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Hoffmann, M.; Bergmann, R. Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning. Algorithms 2022, 15, 27. https://doi.org/10.3390/a15020027
Hoffmann M, Bergmann R. Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning. Algorithms. 2022; 15(2):27. https://doi.org/10.3390/a15020027
Chicago/Turabian StyleHoffmann, Maximilian, and Ralph Bergmann. 2022. "Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning" Algorithms 15, no. 2: 27. https://doi.org/10.3390/a15020027
APA StyleHoffmann, M., & Bergmann, R. (2022). Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning. Algorithms, 15(2), 27. https://doi.org/10.3390/a15020027