Summary of Neuro-symbolic Entity Alignment Via Variational Inference, by Shengyuan Chen et al.
Neuro-Symbolic Entity Alignment via Variational Inference
by Shengyuan Chen, Qinggang Zhang, Junnan Dong, Wen Hua, Jiannong Cao, Xiao Huang
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed NeuSymEA framework combines symbolic and neural models to tackle entity alignment (EA) tasks. By modeling the joint probability of all possible pairs’ truth scores, NeuSymEA can handle uncertainty and provide interpretability through a path-ranking-based explainer. The framework optimizes the joint probability using variational EM algorithm, with an E-step that infers missing alignments via neural models and an M-step that updates rule weights based on observed and inferred alignments. Experiments demonstrate NeuSymEA’s effectiveness and robustness, outperforming baselines in entity alignment tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuSymEA is a new way to connect two big databases of information (knowledge graphs) by finding equivalent items. Right now, there are two main types of methods for doing this: symbolic models and neural networks. Symbolic models are very precise but can struggle with finding connections between complex pieces of information. Neural networks are good at finding patterns, but they don’t always explain why they made a certain decision. NeuSymEA tries to combine the best of both worlds by using a special kind of model that considers all possible connections and then adjusts its thinking based on what it finds. This helps NeuSymEA make accurate decisions and understand why it’s making them. The results show that NeuSymEA is very good at finding equivalent items in knowledge graphs. |
Keywords
» Artificial intelligence » Alignment » Probability
