Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
Authors: Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Wen Zhang, Huajun Chen
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on standard MMKGC benchmarks reveal that our method surpasses 19 of the latest models, underlining its superior performance. We conduct comprehensive experiments with public MMKG benchmarks (Liu et al. 2019a). |
| Researcher Affiliation | Collaboration | 1College of Computer Science and Technology, Zhejiang University 2ZJU-Ant Group Joint Lab of Knowledge Graph 3Ant Group 4School of Software Technology, Zhejiang University 5Zhejiang Key Laboratory of Big Data Intelligent Computing |
| Pseudocode | No | The paper describes methods through textual descriptions and diagrams (Figure 2 provides an overview of the framework) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | In this paper, we employ three public MMKGC benchmarks DB15K (Liu et al. 2019a), MKG-W and MKG-Y (Xu et al. 2022) to evaluate the model performance. |
| Dataset Splits | No | The paper mentions using |
| Hardware Specification | No | The paper states, |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | During training, we set the training epoch to 2000, the batch size to 1024, and the embedding dimension to 256. The max token number m and n are tuned in {4, 8, 12} and the weight λ is tuned in {1, 0.1, 0.01, 0.001}. We optimize the model with Adam (Kingma and Ba 2015) optimizer. |