Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding
Authors: KE LIANG, Yue Liu, Hao Li, Lingyuan Meng, Suyuan Liu, Siwei Wang, sihang zhou, Xinwang Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six datasets show that Rec Piece achieves higher performances but comparable or even fewer parameters compared to previous anchor-based KGE models, indicating that our model can select better anchors in a more scalable way. |
| Researcher Affiliation | Academia | 1National University of Defense Technology, Changsha, China 2Academy of Military Sciences, Beijing, China |
| Pseudocode | No | The paper describes the methodology in detail, but it does not include a structured pseudocode block or an algorithm labeled as such. |
| Open Source Code | No | The authors mention that the source code will be released after the double-blind review. |
| Open Datasets | Yes | Six benchmark datasets are leveraged to evaluate our Rec Piece as same as previous works do [13, 20, 30]. Specifically, FB15k-237 [64], WN18RR [18], Co DEx-L [54], and YAGO3-10 [45] are used for link prediction. The entity classification is carried out on two subsets (5% and 10% labeled) from WD50K [21], and the OGB WIKIKG 2 [23, 20] is the larger KGs for scalability analysis. |
| Dataset Splits | Yes | Datasets Tasks #Entities #Relationship #Edges #Train #Validation #Test FB15K-237 LP 14,505 237 310,079 272,115 17,526 20,438 |
| Hardware Specification | Yes | All experiments are conducted on the server with 4-core Intel(R) Xeon(R) Platinum 8358 CPUs @ 2.60GHZ, a single 80 GB A100 GPU and 64GB RAM with Py Torch [49] libraries. |
| Software Dependencies | No | The paper mentions 'Py Torch [49] libraries' but does not specify a version number for PyTorch or any other relevant software libraries or dependencies. |
| Experiment Setup | Yes | The p( ) for feature preparation is selected as pretrained Node Piece [63] in the first few epochs. Besides, k-means [41, 44] is selected as g( ) for clustering, and the cluster number is set as '#Rel.' in Tab. 9 for different datasets. For a fair comparison, we set anchor numbers k for each dataset as the same as [20], and 2-layer MLP is adopted as f( ) feature propagation. |