OpenBG500 is an open chinese E-commerce and bussiness knowledge graph dataset contained 500 relations. This dataset is refined from the OpenBG, a million-scale multi-modal dataset evolving products and consumption demands in a unified schema. AliOpenKG500 is developed for several knowledge graph embedding evaluations.
The dataset splits all data into 3 parts. Base statistical information is shown in the table below.
| #Relation | #Entity | #Train (opened) | #Valid (opened) | #Test |
|---|---|---|---|---|
| 500 | 249,743 | 1242550 | 5000 | 5000 |
OpenBG500 is available at Google Drive and Baidu Netdisk(password: 78fw). The main derectory of the dataset is as follows.
OpenBG500
├── OpenBG500_train.tsv # Training set
├── OpenBG500_dev.tsv # Validation set
├── OpenBG500_test.tsv # Test set
├── OpenBG500_entity2text.tsv # Description of entities in Chinese
├── OpenBG500_relation2text.tsv # Description of relations in Chinese
└── OpenBG500_example_pred.tsv # Submit example
- Triples
# OpenBG500_train.tsv/OpenBG500_dev.tsv
Head<\t>Relation<\t>Tail<\n>- Description of entities/relations in Chinese
# OpenBG500_entity2text.tsv/OpenBG500_relation2text.tsv
Entity(Relation)<\t>Description of entitie(relation)<\n>- Test and submit
# For OpenBG500_test.tsv, participants are required to predict 10 Tails for one instance. OpenBG500_example_pred.tsv is a submit example.
Head<\t>Relation<\n>
# OpenBG500_example_pred.tsv
Head<\t>Relation<\t>Tail 1<\t>Tail 2<\t>...<\t>Tail 10<\n>$ head -n 3 OpenBG500_train.tsv
ent_135492 rel_0352 ent_015651
ent_020765 rel_0448 ent_214183
ent_106905 rel_0418 ent_121073
- Read the original data:
with open('OpenBG500_train.tsv', 'r') as fp:
data = fp.readlines()
train = [line.strip('\n').split('\t') for line in data]
_ = [print(line) for line in train[:2]]
# ['ent_135492', 'rel_0352', 'ent_015651']
# ['ent_020765', 'rel_0448', 'ent_214183']- Get the map of Entity(Relatioin)-Description:
ent2textandrel2text:
with open('OpenBG500_entity2text.tsv', 'r') as fp:
data = fp.readlines()
lines = [line.strip('\n').split('\t') for line in data]
_ = [print(line) for line in lines[:2]]
# ['ent_101705', '短袖T恤']
# ['ent_116070', '套装']
ent2text = {line[0]: line[1] for line in lines}
with open('OpenBG500_relation2text.tsv', 'r') as fp:
data = fp.readlines()
lines = [line.strip().split('\t') for line in data]
_ = [print(line) for line in lines[:2]]
# ['rel_0418', '细分市场']
# ['rel_0290', '关联场景']
rel2text = {line[0]: line[1] for line in lines}- Transfer the data to description:
train = [[ent2text[line[0]],rel2text[line[1]],ent2text[line[2]]] for line in train]
_ = [print(line) for line in train[:2]]
# ['苦荞茶', '外部材质', '苦荞麦']
# ['精品三姐妹硬糕', '口味', '原味硬糕850克【10包40块糕】']OpenBG Benchmark：Large Scale Open Business Knowledge Graph Benchmark is a benchmark open for a long time. Welcome to submit your result of OpenBG500.
We do some baseline method on this dataset. TransE, DistMult and ComplEx result are based on OpenKE toolkit, KG-BERT and GenKGC results are based our code.
| Method | Hits@1 | Hits@3 | Hits@10 |
|---|---|---|---|
| TransE | 0.207 | 0.340 | 0.531 |
| DistMult | 0.049 | 0.088 | 0.216 |
| ComplEx | 0.053 | 0.120 | 0.266 |
| KG-BERT | 0.023 | 0.049 | 0.241 |
| GenKGC | 0.203 | 0.280 | 0.351 |
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