Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs
Authors: CHEN SHENGYUAN, Yunfeng Cai, Huang Fang, Xiao Huang, Mingming Sun
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On benchmark datasets, we empirically show that Diff Logic surpasses baselines in both effectiveness and efficiency.In this section, we conduct experiments to answer the following research questions. |
| Researcher Affiliation | Collaboration | Shengyuan Chen Department of Computing The Hong Kong Polytechnic University Hung Hom, Hong Kong SAR shengyuan.chen@connect.polyu.hkYunfeng Cai Cognitive Computing Lab Baidu Research 10 Xibeiwang East Rd., Beijing, China caiyunfeng@baidu.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We incorporate four real-world knowledge graph datasets: YAGO3-10, WN18, WN18RR, and Code X (available in three sizes: small, medium, and large), along with a synthetic logic reasoning dataset: Kinship. Dataset statistics and descriptions can be found in Appendix B.1.Table 6: Statistics of real-world knowledge base datasets. |
| Dataset Splits | Yes | Table 6: Statistics of real-world knowledge base datasets. Dataset #Ent #Rel #Train/Valid/Test #RulesThe optimum weight coefficient η is selected by using the validation set. |
| Hardware Specification | Yes | All the runtime experiments are conducted in the same machine with configurations as in Table 9.Table 9: Machine configuration. Component Specification GPU NVIDIA Ge Force RTX 3090 CPU Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz |
| Software Dependencies | No | The paper mentions that models are 'implemented in Python' but does not provide specific version numbers for Python itself or any other relevant software libraries or dependencies. |
| Experiment Setup | No | The paper mentions that 'Hyperparameters for each baseline are taken from their original paper' and 'The optimum weight coefficient η is selected by using the validation set', but it does not explicitly provide concrete hyperparameter values or detailed training configurations for its own model (Diff Logic) in the main text. |