This repository contains the implementation and dataset of our paper ASSET: A Sem-supervised Approach for Entity Typing in Knowledge Graphs.

Fig. 1 Pipleine of our Semantification Process

• In this work, we propose a novel approach for KG entity typing in knowledge graphs that leverages semi-supervised learning from massive unlabeled data.
• Our approach follows a teacher-student learning paradigm that allows combining a small amount of labeled data with a large amount of unlabeled data to boost the performance of downstream tasks: the teacher model annotates the unlabeled data with pseudo labels; then the student is trained on the pseudo-labeled data and a small amount of high-quality labeled data.
• We conduct several experiments on two benchmarking datasets (FB15k-ET and YAGO43k-ET), our results demonstrate that our approach outperforms state-of-the-art baselines in entity-typing task.

python 3.6
scikit-learn 0.24
tensorflow 2.0
scikit-multilearn 0.2.0 
pickle5 0.0.11
pykeen 1.5.0 

You can install all requirements via pip install -r requirements.txt

• In the data folder, you can download the benchmarking datasets FB15k-ET and YAGO43K-ET.

• For ConnectE embedding, we use the source code from its Github repository. For further details, we refer users to follow the installation instructions on the packages' websites.

• If you want to experiment with another knowledge graph embedding model. We recommend Pykeen or Graphvite for generating embeddings for the datasets.

• Furthermore, we provide the preprocessed files used in our experiments in the data/preprocessed files folder that can be used directly to evaluate the baselines and our approach.

• We provide the source code in Python in the folder src. Users can download the code and use it in their favorite IDE, configure it with different models and datasets.

• As examples, we provide two jupyter noteboos with a description for FB15k-ET and YAGP43k-ET in the notebook folder. First, users should install the required libraries, then locate the data files (e.g., the file of pre-trained embedding models and groud-truth labels.)

The following are our optimal values for the hyper-parameters used in the experiments:

epochs = 100            # Maximum number of epochs
patience = 3            # After how many iterations to stop the training
batch_size = 128        # How many sequences in each batch during training
lr = 0.001             # Learning rate of Adam optimizer
Dropout= 0.20          # Dropout rate in the Deep Neural Model 

If you have any feedback or suggestions, feel free to send me an email to [email protected]

TBD