--- a/README.md
+++ b/README.md
@@ -1,50 +1,50 @@
-# Knowledge Graph Neural Network
-This is our implementation for the paper
-> Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma and Xiangxiang Zeng. KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction. IJCAI' 20 accepted.
-
-<img align="center" src="Figure1.png">
-Figure 1 shows the overview of KGNN. It takes the parsed DDI matrix and knowledge graph obtained from preprocessing of dataset as the input. It outputs the interaction value for the drug-drug pair. 
-
-# Requirement
-To run the code, you need the following dependencies:
-* Python == 3.6.6
-* Keras == 2.3.0
-* Tensorflow == 1.13.1
-* scikit-learn == 0.22
-
-# Installation
-You can create a virtual environment using [conda](https://github.com/conda/conda).
-```bash
-conda create -n kgnn python=3.6.6  
-source activate kgnn  
-git clone https://github.com/xzenglab/KGNN.git  
-cd KGNN  
-pip install -r requirement.txt  
-```
-
-# Dataset
-We just provide the preprocessed KG from KEGG-drug dataset owing to the size limited. And you can directly download the original DrugBank dataset ([V5.1.4](https://www.drugbank.ca/releases/5-1-4)). Note that the construction of KG please refer to [Bio2RDF](https://github.com/bio2rdf/bio2rdf-scripts/wiki) tool in detail.
-
-# Usage
-```bash
-python run.py
-```
-
-# Citation
-```bash
-@inproceedings{ijcai2020-380,  
-  title     = {KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction},  
-  author    = {Lin, Xuan and Quan, Zhe and Wang, Zhi-Jie and Ma, Tengfei and Zeng, Xiangxiang},  
-  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}},  
-  publisher = {International Joint Conferences on Artificial Intelligence Organization},               
-  editor    = {Christian Bessiere},    	
-  pages     = {2739--2745},    
-  year      = {2020},  
-  month     = {7},  
-  note      = {Main track},  
-  doi       = {10.24963/ijcai.2020/380},  
-  url       = {https://doi.org/10.24963/ijcai.2020/380},  
-}  
-```
-
-For any clarification, comments, or suggestions please create an issue or contact [Jacklin](Jack_lin@hnu.edu.cn).
+# Knowledge Graph Neural Network
+This is our implementation for the paper
+ Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma and Xiangxiang Zeng. KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction. IJCAI' 20 accepted.
+
+<img align="center" src="https://github.com/xzenglab/KGNN/blob/master/Figure1.png?raw=true">
+Figure 1 shows the overview of KGNN. It takes the parsed DDI matrix and knowledge graph obtained from preprocessing of dataset as the input. It outputs the interaction value for the drug-drug pair. 
+
+# Requirement
+To run the code, you need the following dependencies:
+* Python == 3.6.6
+* Keras == 2.3.0
+* Tensorflow == 1.13.1
+* scikit-learn == 0.22
+
+# Installation
+You can create a virtual environment using [conda](https://github.com/conda/conda).
+```bash
+conda create -n kgnn python=3.6.6  
+source activate kgnn  
+git clone https://github.com/xzenglab/KGNN.git  
+cd KGNN  
+pip install -r requirement.txt  
+```
+
+# Dataset
+We just provide the preprocessed KG from KEGG-drug dataset owing to the size limited. And you can directly download the original DrugBank dataset ([V5.1.4](https://www.drugbank.ca/releases/5-1-4)). Note that the construction of KG please refer to [Bio2RDF](https://github.com/bio2rdf/bio2rdf-scripts/wiki) tool in detail.
+
+# Usage
+```bash
+python run.py
+```
+
+# Citation
+```bash
+@inproceedings{ijcai2020-380,  
+  title     = {KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction},  
+  author    = {Lin, Xuan and Quan, Zhe and Wang, Zhi-Jie and Ma, Tengfei and Zeng, Xiangxiang},  
+  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}},  
+  publisher = {International Joint Conferences on Artificial Intelligence Organization},               
+  editor    = {Christian Bessiere},    	
+  pages     = {2739--2745},    
+  year      = {2020},  
+  month     = {7},  
+  note      = {Main track},  
+  doi       = {10.24963/ijcai.2020/380},  
+  url       = {https://doi.org/10.24963/ijcai.2020/380},  
+}  
+```
+
+For any clarification, comments, or suggestions please create an issue or contact [Jacklin](Jack_lin@hnu.edu.cn).