OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport
Authors: Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on well-established multi-modal knowledge graph completion benchmarks show that our OTKGE achieves state-of-the-art performance. |
| Researcher Affiliation | Collaboration | Zongsheng Cao1,2 Qianqian Xu3 Zhiyong Yang4 Yuan He5 Xiaochun Cao6,1 Qingming Huang4,3,7,8 1 SKLOIS, Institute of Information Engineering, CAS 2 School of Cyber Security, University of Chinese Academy of Sciences 3 Key Lab. of Intelligent Information Processing, Institute of Computing Tech., CAS 4 School of Computer Science and Tech., University of Chinese Academy of Sciences 5 Alibaba Group 6 School of Cyber Science and Tech., Shenzhen Campus, Sun Yat-sen University 7 BDKM, University of Chinese Academy of Sciences 8 Peng Cheng Laboratory |
| Pseudocode | Yes | Algorithm 1: Multi-modal representations fusion. |
| Open Source Code | Yes | 2https://github.com/Lion-ZS/OTKGE |
| Open Datasets | Yes | Dataset. In terms of the link prediction task, we conduct the experiments and evaluate OTKGE with two standard competition benchmarks as shown in Table 1. There includes multi-modal datasets: WN9-IMG [41] and FB-IMG [19]. |
| Dataset Splits | Yes | Table 1: Statistics of the datasets used in this paper. (Nume represents the number of entities and Numr represents the number of relations.) Dataset ... Training Validation Test |
| Hardware Specification | No | In the course of the experiment, we implement OTKGE2 with Py Torch and conduct experiments with a single GPU. |
| Software Dependencies | No | In the course of the experiment, we implement OTKGE2 with Py Torch and conduct experiments with a single GPU. |
| Experiment Setup | Yes | Specifically, the embedding size k is searched in {100, 200, 400, 500} and the learning rate is searched in {0.001, 0.005, 0.01, 0.05, 0.1}. |