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 | Conference PDF | Archive PDF | Plain Text | 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 {huangyx, daiwz, jiangy, zhouzh}@lamda.nju.edu.cn, {zqsun, gyli, xbtian}.nju@gmail.com, whu@nju.edu.cn |
| 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. |