Summary of Twig: Towards Pre-hoc Hyperparameter Optimisation and Cross-graph Generalisation Via Simulated Kge Models, by Jeffrey Sardina et al.
TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models
by Jeffrey Sardina, John D. Kelleher, Declan O’Sullivan
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces TWIG (Topologically-Weighted Intelligence Generation), a novel approach for simulating the output of knowledge graph embeddings (KGEs) that uses a tiny fraction of parameters. Unlike traditional KGE models, TWIG learns weights from topological features of graph data without requiring latent representations of entities or edges. The authors demonstrate the effectiveness of TWIG on the UMLS dataset, showing that a single neural network can accurately predict the results of state-of-the-art ComplEx-N3 KGE model across all hyperparameter configurations using only 2590 learnable parameters. This is in contrast to traditional KGE models which require a combined cost of 29,322,000 parameters to achieve similar performance. The authors make two claims: that KGEs do not learn latent semantics but rather structural patterns, and that hyperparameter choice in KGEs is deterministic based on the model and graph structure. They also formulate their findings under the “Structural Generalisation Hypothesis”, suggesting that embedding-free data-structure-based learning methods can simulate KGE performance across diverse domains and with different semantics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows a new way to understand how computers learn from complex data structures like graphs. Instead of using complicated mathematical formulas, TWIG uses simple patterns found in the graph data to make predictions. The authors tested this approach on a big dataset and showed that it can work just as well as more complicated methods. They also suggest that this approach could be used to solve other problems with similar datasets. |
Keywords
* Artificial intelligence * Embedding * Hyperparameter * Knowledge graph * Neural network * Semantics
