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Project README
DSKG
DSKG: Deep Sequential models for Knowledge Graphs
Requirements
- python 3.x
- tensorflow 1.x
- numpy, pandas
- jupyter
Running
-
unpack the data.tar.gz, which includes all of three datasets.
-
run jupyter:
jupyter notebook -
open runDSKG.ipynb & run all cells (Kernel -> Restart & Run All)
You can also directly click runDSKG.ipynb in this page to preview the results we have run.
Entity Prediction Results
FB15K-237
| Models | Hits@1 | Hits@10 | MRR | MR |
|---|---|---|---|---|
| TransE (our) | 13.3 | 40.9 | 22.3 | 315 |
| TransR (our) | 10.9 | 38.2 | 19.9 | 417 |
| PTransE (our) | 21.0 | 50.1 | 31.4 | 299 |
| DISTMULT | 15.5 | 41.9 | 24.1 | 254 |
| NLFeat | - | 41.4 | 27.2 | - |
| ComplEx | 15.2 | 41.9 | 24.0 | 248 |
| NeuralLP | - | 36.2 | 24.0 | - |
| ConvE | 23.9 | 49.1 | 31.6 | 246 |
| InverseModel | 0.4 | 1.2 | 0.7 | 7,124 |
| DSKG (cascade) | 20.5 | 50.1 | 30.3 | 842 |
| DSKG | 24.9 | 52.1 | 33.9 | 175 |
FB15K
| Models | Hits@1 | Hits@10 | MRR | MR |
|---|---|---|---|---|
| TransE (our) | 30.5 | 73.7 | 45.8 | 71 |
| TransR (our) | 37.7 | 76.7 | 51.9 | 84 |
| PTransE (our) | 63.8 | 87.2 | 73.1 | 59 |
| DISTMULT | 54.6 | 82.4 | 65.4 | 97 |
| NLFeat | - | 87.0 | 82.1 | - |
| ComplEx | 59.9 | 84.0 | 69.2 | - |
| NeuralLP | - | 83.7 | 76.0 | - |
| ConvE | 67.0 | 87.3 | 74.5 | 64 |
| InverseModel | 74.3 | 78.6 | 75.9 | 1,563 |
| DSKG (cascade) | 64.9 | 87.7 | 73.0 | 151 |
| DSKG | 75.3 | 90.2 | 80.9 | 30 |
WN18
| Models | Hits@1 | Hits@10 | MRR | MR |
|---|---|---|---|---|
| TransE (our) | 27.4 | 94.4 | 57.8 | 431 |
| TransR (our) | 54.8 | 94.7 | 72.6 | 415 |
| PTransE (our) | 87.3 | 94.2 | 90.5 | 516 |
| DISTMULT | 72.8 | 93.6 | 82.2 | 902 |
| NLFeat | - | 94.3 | 94.0 | - |
| ComplEx | 93.6 | 94.7 | 94.1 | - |
| NeuralLP | - | 94.5 | 94.0 | - |
| ConvE | 93.5 | 95.5 | 94.2 | 504 |
| InverseModel | 75.7 | 96.9 | 85.7 | 602 |
| DSKG (cascade) | 93.9 | 95.0 | 94.3 | 959 |
| DSKG | 94.2 | 95.2 | 94.6 | 337 |
Citation
Lingbing Guo, Qingheng Zhang, Weiyi Ge, Wei Hu*, Yuzhong Qu. DSKG: A Deep Sequential Model for Knowledge Graph Completion. In: CCKS 2018. https://arxiv.org/pdf/1810.12582.pdf
Lingbing Guo, Qingheng Zhang, Wei Hu∗, Zequn Sun, Yuzhong Qu. Learning to Complete Knowledge Graphs with Deep Sequential Models. Data Intelligence, 1(3):224–243, 2019. http://www.data-intelligence.org/p/25/
Open Source Agenda is not affiliated with "DSKG" Project. README Source: nju-websoft/DSKG
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Open Issues
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Last Commit
4 years ago
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