Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport
Authors: Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang
NeurIPS 2022 | Venue PDF | 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}. |