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..
Enabling Abductive Learning to Exploit Knowledge Graph
Authors: Yu-Xuan Huang, Zequn Sun, Guangyao Li, Xiaobin Tian, Wang-Zhou Dai, Wei Hu, Yuan Jiang, Zhi-Hua Zhou
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four different tasks show that ABL-KG can automatically extract useful rules from large-scale and noisy knowledge graphs, and significantly improve the performance of machine learning with only a handful of labeled data. |
| Researcher Affiliation | Academia | Yu-Xuan Huang , Zequn Sun , Guangyao Li , Xiaobin Tian , Wang-Zhou Dai , Wei Hu , Yuan Jiang and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China EMAIL, {zqsun, gyli, xbtian}.EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Abductive Learning with Knowledge Graph |
| Open Source Code | Yes | The code is available for download1. 1https://github.com/AbductiveLearning/ABL-KG |
| Open Datasets | Yes | The zoo animal classification dataset [Dua and Graff, 2017] contains animals attributes (e.g., backbone, legs) and their categories (e.g., bird, fish), along with the names of each attribute and class (task specification). We consider the widely-used cross-lingual dataset DBP-EN-FR, which was proposed in the Open EA benchmark study [Sun et al., 2020]. We consider the benchmark dataset FB15K-237 [Toutanova and Chen, 2015] and choose the most popular model, Trans E [Bordes et al., 2013], as the basic learning model in the experiment. The task s input is images from ADE20K [Zhou et al., 2017]. |
| Dataset Splits | Yes | We follow the training/validation/test data splits of FB15K-237, and report the average test results of five runs in Table 4. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were found. |
| Software Dependencies | No | The paper mentions software like GloVe, Random Forest, Align E, Trans E, ResNet-50, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | Rules are mined using our algorithm in Section 4.3, where d is set to 2, and we set the confidence of Is A relation to be 1.0 and others 0.9. The entity alignment model learns embeddings for the two KGs to measure entity similarity. In our experiment, we consider the widely-used cross-lingual dataset DBP-EN-FR, which was proposed in the Open EA benchmark study [Sun et al., 2020]. Align E+ employs self-training [Yarowsky, 1995] and selects the predicted entity alignment pairs whose embedding similarity is greater than 0.9 to augment training data. In our task, only 5% or 10% of all 20k images are labeled. |