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..
Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs
Authors: Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on two real-world datasets DBP15K and DWY100K, and the experimental results show that the proposed model NAEA significantly and consistently outperforms state-of-the-art entity alignment models. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China 2School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China 3Department of Computing, Macquarie University, Sydney, Australia |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations but does not provide a structured pseudocode or algorithm block. |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository was found in the paper. |
| Open Datasets | Yes | We conduct experiments on two real-world datasets DBP15K and DWY100K. DBP15K [Sun et al., 2017] is selected from the multilingual versions of DBpedia that includes entity alignment links from entities of English version to those in other languages. [...] DWY100K [Sun et al., 2018] is built from three large-scale multi-lingual knowledge graph DBpedia, Wikidata and YAGO3. |
| Dataset Splits | Yes | In this experiment, we randomly select monolingual triplets from DBP15KZH-EN and DBP-WD to organize the training, valid and test set according to ratio 8 : 1 : 1. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | In our model, we set the maximum number of neighbors n as 200, and select the dimension of entity(relation) embeddings m from {50, 75, 100, 150, 200}, the learning rate η from {0.001, 0.01, 0.1}, β from {0, 0.2, 0.4, 0.6, 0.8, 1}, λ from {0.1, 0.5, 1, 1.5, 2}, µ1 from {0.5, 1, 2, 3, 4}, µ2 from {0.01, 0.1, 0.5, 0.8, 1, 1.5, 2}, γ from {0.1, 0.5, 1, 1.5, 2, 2.5}, the number of head K from {1, 2, 4, 6, 8}. For our model, the best optimal parameter configurations are m = 75, β = 0.8, λ = 1, µ1 = 1, µ2 = 0.1, γ = 2, K = 4, η = 0.01. For each positive triplet, we select 10 negative triples for training, and set the training epochs as 1000. |