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..
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
Authors: Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yin Fang, Wen Zhang, Huajun Chen
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to validate the performance of GEEA, where it achieves state-of-the-art performance in entity alignment and generates high-quality new entities in entity synthesis. The source code and datasets are available at github.com/zjukg/GEEA. |
| Researcher Affiliation | Collaboration | 1College of Computer Science and Technology, Zhejiang University 2Zhejiang University Ant Group Joint Laboratory of Knowledge Graph 3Alibaba-Zhejiang University Joint Reseach Institute of Frontier Technologies 4Department of Computer Science, The University of Manchester 5School of Software Technology, Zhejiang University |
| Pseudocode | Yes | Algorithm 1 Generative Embedding-based Entity Alignment |
| Open Source Code | Yes | The source code and datasets are available at github.com/zjukg/GEEA. |
| Open Datasets | Yes | We used the multi-modal EEA benchmarks (DBP15K (Sun et al., 2017), FB15K-DB15K and FB15K-YAGO15K (Chen et al., 2020)) as datasets, excluding surface information (i.e., the textual label information) to prevent data leakage (Sun et al., 2020b; Chen et al., 2022b). |
| Dataset Splits | Yes | An EEA model M uses a small number of aligned entity pairs S (a.k.a., seed alignment set) as training data to infer the remaining alignment pairs T in the testing set. ... until the performance on the valid dataset does not improve. |
| Hardware Specification | Yes | We implement GEEA with Py Torch and run the main experiments on a RTX 4090. |
| Software Dependencies | No | We implement GEEA with Py Torch and run the main experiments on a RTX 4090. The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | The overall hyper-parameter settings in the main experiments are presented in Table 6. |