DSKG

DSKG: Deep Sequential models for Knowledge Graphs

Requirements

• python 3.x
• tensorflow 1.x
• numpy, pandas
• jupyter

Running

1. unpack the data.tar.gz, which includes all of three datasets.

2. run jupyter: jupyter notebook

3. 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/