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..
Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs
Authors: Yuanning Cui, Yuxin Wang, Zequn Sun, Wenqiang Liu, Yiqiao Jiang, Kexin Han, Wei Hu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments regarding link prediction accuracy, knowledge transfer capability, and learning efficiency to validate the proposed model, LKGE. The datasets and source code are available at https://github.com/nju-websoft/LKGE. |
| Researcher Affiliation | Collaboration | Yuanning Cui1, Yuxin Wang1, Zequn Sun1, Wenqiang Liu3, Yiqiao Jiang3, Kexin Han3, Wei Hu1,2* 1 State Key Laboratory for Novel Software Technology, Nanjing University, China 2 National Institute of Healthcare Data Science, Nanjing University, China 3 Interactive Entertainment Group, Tencent Inc, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The datasets and source code are available at https://github.com/nju-websoft/LKGE. |
| Open Datasets | Yes | To simulate a variety of aspects of KG growth, we create four datasets based on FB15K-237 (Toutanova and Chen 2015), which are entity-centric, relation-centric, fact-centric, and hybrid. We denote them by ENTITY, RELATION, FACT and HYBRID, respectively. ... The datasets and source code are available at https://github.com/nju-websoft/LKGE. |
| Dataset Splits | Yes | For each snapshot, we randomly divide the new fact set T i into a training set Di, a validation set Vi and a test set Qi by a split ratio of 3:1:1. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'Trans E' but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For a fair comparison, we first tune the hyperparameters of the base model using grid-search: learning rate in {0.0005, 0.0001, 0.001}, batch size in {1024, 2048}, embedding dimension in {100, 200}. Then, we use the same base model for LKGE and all competitors, and tune other hyperparameters. For the regularization models, the α of regularization loss is in {0.01, 0.1, 1.0}. For our model, the β of MAE loss is in {0.01, 0.1, 1.0}. For all competitors, we use Adam optimizer and set the patience of early stopping to 3. |