FS-KEN: Few-shot Knowledge Graph Reasoning by Adversarial Negative Enhancing
Authors: Lingyuan Meng, Ke Liang, Zeyu Zhu, Xinwang Liu, Wenpeng Lu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments conducted on two few-shot knowledge graph completion datasets reveal that FS-KEN surpasses other baseline models, achieving state-of-the-art results. [...] Section 4 Experiments and Discussion |
| Researcher Affiliation | Academia | 1National University of Defense Technology 2Shandong Computer Science Center(National Supercomputer Center in Jinan) EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology and framework with textual explanations and an illustration (Figure 2), but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We assessed the FS-KEN on two real-world few-shot datasets, i.e., NELL-One [Mitchell et al., 2018] and FB15K-237 [Bollacker et al., 2008]. |
| Dataset Splits | Yes | For the NELL-One, the meta-evaluation and meta-test splits provided in the dataset were used for evaluating and testing few-shot tasks. [...] In the case of FB15K-237 [Bollacker et al., 2008], a minority ratio of 7:30 was selected for the target few-shot evaluation and test tasks. [...] Moreover, each test triplet is compared with 50 possible negative triplets. |
| Hardware Specification | Yes | The FS-KEN experiments were primarily executed using the PyTorch [Paszke et al., 2019] library and were performed on a single NVIDIA GeForce 3090Ti. |
| Software Dependencies | No | The FS-KEN experiments were primarily executed using the PyTorch [Paszke et al., 2019] library. No specific version number for PyTorch or other libraries is mentioned. |
| Experiment Setup | Yes | Furthermore, the few-shot instance count K was configured to 3. [...] For the step of generating closed subgraphs, we generate 2-hop subgraphs in NELL-One and 1-hop subgraphs in FB15K-237. We employed AdamW with a learning rate of 1e-5. Moreover, the training epochs of model were set to 5000, and the training batch size was set to 8. [...] The experimental results show that when λ1 and λ2 both take the value of 0.1, the model achieves the best performance on NELL-One dataset. For the FB15k-237 dataset, our model achieves best performance when λ1 = 1 and λ2 = 0.1. |