Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
Authors: Jiaxin Bai, Xin Liu, Weiqi Wang, Chen Luo, Yangqiu Song
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments To ensure a fair comparison of various methods for the CEQA problem, we generated a dataset by sampling from ASER [53], the largest eventuality knowledge graph, which encompasses fourteen types of discourse relations. |
| Researcher Affiliation | Collaboration | Jiaxin Bai Department of CSE HKUST jbai@connect.ust.hk Xin Liu Department of CSE HKUST xliucr@cse.ust.hk Weiqi Wang Department of CSE HKUST wwangbw@cse.ust.hk Chen Luo Amazon.com Inc cheluo@amazon.com Yangqiu Song Department of CSE HKUST yqsong@cse.ust.hk |
| Pseudocode | Yes | Algorithm 1 The algorithm used for sampling a complex query from a knowledge graph starting from a random vertex v from the knowledge graph G with query structure T. |
| Open Source Code | Yes | Code and data are publicly available 3. 3https://github.com/HKUST-KnowComp/CEQA |
| Open Datasets | Yes | To ensure a fair comparison of various methods for the CEQA problem, we generated a dataset by sampling from ASER [53], the largest eventuality knowledge graph, which encompasses fourteen types of discourse relations... The eventuality knowledge graph, ASER-50K, is derived from a sub-sample of ASER2.14. 4https://hkust-knowcomp.github.io/ASER/html/index.html |
| Dataset Splits | Yes | The division of edges within each knowledge graph into training, validation, and testing sets was performed in an 8:1:1 ratio, as illustrated in Table 5. |
| Hardware Specification | Yes | All the experiments can be run on NVIDIA RTX3090 GPUs. |
| Software Dependencies | No | The paper does not explicitly provide specific version numbers for software dependencies or libraries (e.g., 'Python 3.8, PyTorch 1.9'). It only generally refers to models or frameworks without version details. |
| Experiment Setup | Yes | We use the same number of embedding sizes of three hundred for all models and use grid-search to tune the hyperparameters of the learning rate ranging from {0.002, 0.001, 0.0005, 0.0002, 0.0001} and batch size ranging from {128, 256, 512}. |