These codes implement UKGsE model by pytorch and gensim in the python language, which provides the fast but effective Knowledge Graphs Embedding and more accurate confidence prediction on uncertainty of relation facts in KG. Some kinds of approximate knowledge reasoning can also be done in the Uncertain Knowledge Graph Embedding (UKGE) space. Now we are working on Question-Answering system by means of this model.
Local environment should be equal to or above as following:
python 3.6
Keras 2.3.1 (with Theano 1.0.1 backend)
gensim 3.8.3
To run the experiments, use:
python ./src/ukgse.py
or
python ./src/ukgse.py --dataset ppi5k --dimension 128 --batchsize 128 --epochs 200
Here two experiment datasets, CN15k and PPI5k, are provided in separate folders.
cn15k:
train.tsv # each line likes 'head_id, relation_id, tail_id, confidence value'
test.tsv # same as above
entity_id.csv # each line likes 'entity_name, entity_id'
relation_id.csv # same as above
ppi5k:
train.tsv
test.tsv
entity_id.csv
relation_id.csv
@inproceedings{yang2020fast,
title={Fast Confidence Prediction of Uncertainty based on Knowledge Graph Embedding},
author={Yang, Shihan and Zhang, Weiya and Tang, Rui},
booktitle={2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3446132.3446186},
year={2020}
}