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..
Multimodal Analogical Reasoning over Knowledge Graphs
Authors: Ningyu Zhang, Lei Li, Xiang Chen, Xiaozhuan Liang, Shumin Deng, Huajun Chen
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (Mar T) motivated by the structure mapping theory, which can obtain better performance. |
| Researcher Affiliation | Academia | 1Zhejiang University, AZFT Joint Lab for Knowledge Engine 2National University of Singapore EMAIL |
| Pseudocode | No | The paper describes its methods and includes mathematical equations, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and datasets are available in https://github.com/zjunlp/MKG_Analogy. |
| Open Datasets | Yes | Specifically, we construct a Multimodal Analogical Reasoning data Set (MARS) and a multimodal knowledge graph Mar KG and a multimodal knowledge graph Mar KG to support this task. Code and datasets are available in https://github.com/zjunlp/MKG_Analogy. with linked external entities in Wikidata and images from Laion-5B (Schuhmann et al., 2021). |
| Dataset Splits | Yes | MARS has 10,685 training, 1,228 validation and 1,415 test instances, which are more significant than previous language analogy datasets. |
| Hardware Specification | Yes | We utilize Pytorch to conduct all experiments with 1 Nvidia 3090 GPU. |
| Software Dependencies | No | The paper mentions using 'Pytorch' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The details of hyper-parameters can be seen in Table 8. Hyper-parameters MKGE Baselines MPT Baselines epoch {300, 1000} 15 sequence length 128 learning rate {1e-2, 5e-3} {3e-5, 4e-5, 5e-5} batch size 1000 64 optimizer {Adagrad, SGD} Adam W adam epsilon 1e-8 λ {0.38, 0.43, 0.45} |