Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.1.1. Dataset and Settings
2.1.2. Experimental Environment
2.2. Methods
2.2.1. KG Embedding Generator
2.2.2. Dependent Syntactic Analysis Module
2.2.3. Graph Convolutional Network
2.2.4. Answer Scoring Module
3. Results
3.1. Comparative Experiments
3.2. Ablation Study
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset | Training Set | Test Set | Validation Set |
---|---|---|---|
CommonsenseQA | 9741 | 1140 | 1221 |
OpenbookQA | 4957 | 500 | 500 |
Computer Information | Operating System | Windows 10 64-bit |
CPU | Intel(R) Core (TM) i5-8265U CPU @ 1.60 GHz (8 CPUs) ~1.8 GHz | |
GPU | RTX 3060 | |
RAM | 16 GB | |
Toolkit | Python 3.7 | Numpy 1.21.5 |
Scikit_Learn 1.0.2 | ||
Pandas 0.25.1 | ||
Torch 1.12.0 | ||
Matplotlib 3.5.2 |
Model | CommonsenseQA | OpenbookQA | ||
---|---|---|---|---|
IHdev-Acc. (%) | IHtest-Acc. (%) | Dev-Acc. (%) | Test-Acc. (%) | |
R-GCN * [35] | 56.72 (±0.42) | 53.90 (±0.62) | 63.51 (±1.81) | 61.83 (±1.60) |
GconAttn * [36] | 56.37 (±0.72) | 53.64 (±0.78) | 62.62 (±1.07) | 61.21 (±2.14) |
KagNet * [37] | 55.77 (±0.50) | 56.39 (±0.53) | 64.77 (±1.17) | 61.83 (±2.05) |
MHGRN * [38] | 60.12 (±0.33) | 56.93 (±0.72) | 67.40 (±1.33) | 66.15 (±1.45) |
Rce-KGQA * [32] | 61.52 (±0.42) | 59.18 (±0.63) | 67.72 (±1.13) | 66.45 (±1.29) |
DSSAGN framework * | 63.22 (±0.20) | 62.35 (±0.45) | 68.52 (±0.93) | 67.38 (±1.05) |
Model | CommonsenseQA | OpenbookQA | ||
---|---|---|---|---|
Training Time (min) | Testing Time (min) | Training Time (min) | Testing Time (min) | |
R-GCN * [35] | 25.90 | 6.23 | 24.85 | 4.66 |
GconAttn * [36] | 24.66 | 5.68 | 23.60 | 4.08 |
KagNet * [37] | 22.05 | 4.88 | 19.55 | 3.26 |
MHGRN * [38] | 21.25 | 4.45 | 19.03 | 3.05 |
Rce-KGQA * [32] | 19.30 | 3.85 | 17.26 | 2.43 |
DSSAGN framework * | 21.21 | 4.33 | 17.21 | 2.12 |
Model | CommonsenseQA | OpenbookQA | ||
---|---|---|---|---|
IHdev-Acc. (%) | IHtest-Acc. (%) | Dev-Acc. (%) | Test-Acc. (%) | |
Model 1 | 61.35 (±0.38) | 60.52 (±0.73) | 66.40 (±1.64) | 65.65 (±1.88) |
Model 2 | 61.58 (±0.42) | 61.88 (±0.67) | 66.90 (±1.35) | 65.91 (±1.47) |
Model 3 | 62.67 (±0.25) | 61.93 (±0.36) | 67.89 (±1.03) | 66.31 (±1.21) |
DSSAGN framework | 63.22 (±0.20) | 62.35 (±0.45) | 68.52 (±0.93) | 67.38 (±1.05) |
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Cai, S.; Ma, Q.; Hou, Y.; Zeng, G. Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks. Electronics 2024, 13, 1436. https://doi.org/10.3390/electronics13081436
Cai S, Ma Q, Hou Y, Zeng G. Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks. Electronics. 2024; 13(8):1436. https://doi.org/10.3390/electronics13081436
Chicago/Turabian StyleCai, Songtao, Qicheng Ma, Yupeng Hou, and Guangping Zeng. 2024. "Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks" Electronics 13, no. 8: 1436. https://doi.org/10.3390/electronics13081436