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..
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Authors: Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations. |
| Researcher Affiliation | Academia | 1Institute of Computer Science and Technology, Peking University, China 2School of Computing and Communications, Lancaster University, U. K. EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate our approach on three large-scale cross-lingual datasets from DBP15K [Sun et al., 2017]. glove.840B.300d 1 http://nlp.stanford.edu/projects/glove/ |
| Dataset Splits | No | We use the same training/testing split with previous works [Sun et al., 2018], 30% for training and 70% for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'glove.840B.300d' for word vectors but does not specify any software environments or library versions used for implementation. |
| Experiment Setup | Yes | The configuration we used is: β1 = 0.1, β2 = 0.3, and γ = 1.0. The dimensions of hidden representations in dual and primal attention layers are d = 300, d = 600, and d = 300. All dimensions of hidden representations in GCN layers are 300. The learning rate is set to 0.001 and we sample K = 125 negative pairs every 10 epochs. |