Neural Compositional Rule Learning for Knowledge Graph Reasoning
Authors: Kewei Cheng, Nesreen Ahmed, Yizhou Sun
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that NCRL learns high-quality rules, as well as being generalizable. Specifically, we show that NCRL is scalable, efficient, and yields state-of-the-art results for knowledge graph completion on large-scale KGs. |
| Researcher Affiliation | Collaboration | Kewei Cheng Department of Computer Science, UCLA viviancheng@cs.ucla.edu Nesreen K. Ahmed Intel Labs nesreen.k.ahmed@intel.com Yizhou Sun Department of Computer Science, UCLA yzsun@cs.ucla.edu |
| Pseudocode | Yes | Algorithm 1: Learning Algorithm |
| Open Source Code | Yes | Source code is available at https://github.com/vivian1993/NCRL.git. |
| Open Datasets | Yes | We use six widely used benchmark datasets to evaluate our NCRL in comparison to SOTA methods from knowledge graph embedding and rule learning methods. Specifically, we use the Family (Hinton et al., 1986), UMLS (Kok & Domingos, 2007), Kinship (Kok & Domingos, 2007), WN18RR (Dettmers et al., 2018), FB15K-237 (Toutanova & Chen, 2015), YAGO3-10 (Suchanek et al., 2007) datasets. |
| Dataset Splits | No | The paper mentions training and testing on different 'hops' for systematic generalization and evaluating on 'test triples', but it does not specify explicit percentages for train/validation/test splits, nor does it explicitly mention a 'validation set' or its size/percentage for the KG completion task. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only vaguely mentions 'memory capacity of our machines'. |
| Software Dependencies | No | The paper discusses models like RNN, LSTM, GRU, and Transformer, but it does not provide specific version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | In our implementation, we vary the size of the sliding window among {2, 3}. We vary the dimension of relation embeddings among {10, 100, 200, 500, 1000, 2000}. We generate k rules with the highest qualities per query relation and use them to predict missing links. We vary k among {10, 20, 40, 60, 80, 100}. |