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..
Iterative Entity Alignment via Joint Knowledge Embeddings
Authors: Hao Zhu, Ruobing Xie, Zhiyuan Liu, Maosong Sun
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on realworld datasets show that, as compared to baselines, our method achieves significant improvements on entity alignment |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and Technology, Tsinghua University, Beijing, China 2 Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009 China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code can be obtained from https://github.com/thunlp/IEAJKE. |
| Open Datasets | Yes | In this paper, we build four datasets based on FB15K [Bordes et al., 2013] originally extracted from Freebase [Bordes et al., 2013] |
| Dataset Splits | Yes | The alignments of other entities are used as the test set and validation set. ... Table 1: Statistics of DFB-1, DFB-2 and DFB-3 [shows #Valid column] |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper mentions optimizers (SGD) and specific measures (L1-norm) but does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks like Python, PyTorch, or TensorFlow versions). |
| Experiment Setup | Yes | As for hyper-parameters, we select margin γ among {0.5, 1.0, 1.5, 2.0}. We set the dimensions of entity and relation embeddings to be the same n. We set a fixed learning rate λ = 0.001 following [Bordes et al., 2013; Lin et al., 2015]. For Hard Alignment and Soft Alignment, we select θ among {0.5, 1.0, 2.0, 3.0, 4.0}. For Soft Alignment, we select k among {0.5, 1.0, 2}. For a fair comparison, all models are trained under the same dimension n = 50 and the same amount of epochs 3000. The optimal configurations of our models are: γ = 1.0, k = 1.0, B = {1000, 1500, 2000, 2500}, C = {5000, 6000, 7000, 8000}, θ = 1.0 for Hard Alignment and θ = 3.0 for Soft Alignment. |