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README.md

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-# ASSET
-A Semi-supervised Approach for Entity Typing in Knowledge Graphs
+## ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs
+This repository contains the implementation and dataset of our paper *ASSET: A Sem-supervised Approach for Entity Typing in Knowledge Graphs*. 
+
+## Summary
+- 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 performance: 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). 
+
+## Requirements
+```
+python 3.6
+scikit-learn 0.24
+tensorflow 2.0
+scikit-multilearn 0.2.0 
+pickle5 0.0.11
+pykeen 1.5.0 
+```
+## Installation
+You can install all requirements via ```pip install -r requirements.txt```
+
+## Datasets
+
+* 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](https://github.com/Adam1679/ConnectE). 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](https://pykeen.github.io/) or [Graphvite](https://graphvite.io/) 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.
+
+## How to run
+- We provide a jupyter notebook with a description for each dataset 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.)
+- We provide also the source code in Python in the folder ```src```. Users can download the code and use it in their favorite IDE.
+- For fast running and reproduce our results, we provide some scripts that users can run in the command-line to reproduce our results easily.
+```
+python scripts/FB15K_ET.py # for evaluation on FB15K dataset
+python scripts/YAGO_ET.py # for evaluation on YAG43K dataset
+```
+## Hyper-parameters
+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= 0.25 # Dropout rate in the Deep Neural Model 
+```
+
+## Contact
+If you have any feedback or suggestions, feel free to send me an email to [email protected] 
+
+## Cite
+TBD