Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning
Authors: Huijuan Wang, Siming Dai, Weiyue Su, Hui Zhong, Zeyang Fang, Zhengjie Huang, Shikun Feng, Zeyu Chen, Yu Sun, Dianhai Yu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments also demonstrate that REP has significant scalability while improving or maintaining prediction quality. Notably, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBLWiki KG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE. |
| Researcher Affiliation | Industry | Huijuan Wang , Siming Dai , Weiyue Su , Hui Zhong , Zeyang Fang , Zhengjie Huang , Shikun Feng , Zeyu Chen , Yu Sun and Dianhai Yu Baidu, Inc. {wanghuijuan03, daisiming, suweiyue, zhonghui03, fangzeyang, huangzhengjie, fengshikun01, chenzeyu01, sunyu02, yudianhai}@baidu.com |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and details of REP are released in Graph4KG 3. https://github.com/PaddlePaddle/PGL/tree/main/apps/Graph4KG |
| Open Datasets | Yes | OGBL-Wiki KG2 [Hu et al., 2020] is a link prediction dataset from Open Graph Benchmark (OGB) 2. Wiki KG90M-LSC [Hu et al., 2021] of KDD Cup 2021 1 is extracted from Wikidata. |
| Dataset Splits | Yes | Wiki KG90M-LSC... provides 1000 randomly sampled entity candidates for each tail in the validation/test set. OGBL-Wiki KG2... 1000 randomly sampled candidates for tail entities are provided likewise. |
| Hardware Specification | Yes | Experiments were conducted on Intel Xeon Gold 6271C CPUs and Tesla V100 SXM2 GPUs. |
| Software Dependencies | No | The paper mentions using "public code to reproduce triplet-based methods" and their own code on "PaddlePaddle/PGL" but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | To investigate the impact of α, we set α as values ranging from 0.95 to 0.99 with step 0.01. We investigate how multi-hop neighbors influence embedding quality by changing the number of hops from 1 to 20. We set α = 0.98 to control variables. |