Knowledge Graph Completion with Relation-Aware Anchor Enhancement
Authors: Duanyang Yuan, Sihang Zhou, Xiaoshu Chen, Dong Wang, Ke Liang, Xinwang Liu, Jian Huang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of our extensive experiments not only validate the efficacy of RAA-KGC but also reveal that by integrating our relation-aware anchor enhancement strategy, the performance of current leading methods can be notably enhanced without substantial modifications. |
| Researcher Affiliation | Academia | 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China 2College of Computer Science and Technology, National University of Defense Technology, Changsha, China EMAIL |
| Pseudocode | No | The paper describes the method using definitions, equations, and textual explanations, but does not include any explicit pseudocode blocks or algorithms formatted as such. |
| Open Source Code | Yes | Code https://github.com/Dayana Yuan/RAA-KGC |
| Open Datasets | Yes | We evaluate RAA-KGC on three commonly used datasets: WN18RR (Dettmers et al. 2018), FB15k-237 (Toutanova and Chen 2015), Wikidata5M-Trans (Wang et al. 2021b). |
| Dataset Splits | Yes | Dataset train valid test WN18RR 86,835 3034 3134 FB15k-237 272,115 17,535 20,466 Wikidata5M-Trans 20,614,279 5,163 5,163 |
| 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. |
| Software Dependencies | Yes | Following the current state-of-the-art KGC methods (Wang et al. 2022a), we use two encoders, g1( ) and g2( ), both initialized with the bert-base-uncased model but do not share parameters. ... We implement RAA-KGC based on the Py Torch library (Paszke et al. 2019). |
| Experiment Setup | Yes | The batch size is 32. As for the specific hyperparameters used in our work, we search the upper bound of the trade-off weight α for contrastive loss within the range {0.1, 0.2, 0.3, 0.4, 0.5}. |