InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Authors: Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that INGRAM outperforms 14 different state-of-the-art methods on varied inductive learning scenarios. |
| Researcher Affiliation | Academia | 1School of Computing, KAIST, Daejeon, South Korea. Correspondence to: Joyce Jiyoung Whang <jjwhang@kaist.ac.kr>. |
| Pseudocode | Yes | Algorithm 1 Embeddings via INGRAM at Inference Time |
| Open Source Code | Yes | 1https://github.com/bdi-lab/In Gram |
| Open Datasets | Yes | We create 12 datasets using three benchmarks, NELL995 (Xiong et al., 2017),Wikidata68K (Gesese et al., 2022), and FB15K237 (Toutanova & Chen, 2015). |
| Dataset Splits | Yes | Einf is divided into three pairwise disjoint sets, such that Einf := Finf Tval Ttest with a ratio of 3:1:1. ... We divide Etr into Ftr and Ttr with a ratio of 3:1. |
| Hardware Specification | Yes | All experiments were conducted with Ge Force RTX 2080 Ti, Ge Force RTX 3090 or RTX A6000, depending on the implementations of each method. |
| Software Dependencies | No | The paper mentions using 'the official C++ implementation of node2vec' but does not provide specific version numbers for this or any other software component used in the experiments. |
| Experiment Setup | Yes | We set d = 32 and bd = 32 for INGRAM and all the baseline methods. ... We tuned INGRAM with 10 negative samples, d {32, 64, 128, 256}, bd {128, 256}, L {1, 2, 3}, b L {2, 3, 4}, K {8, 16}, b K {8, 16}, γ {1.0, 1.5, 2.0, 2.5}, B {1, 5, 10} and the learning rate {0.0005, 0.001}. |