Update README.md

README.md

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 license: gpl-3.0
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+# GraphMatcher
+The GraphMatcher aims to find the correspondes between two ontologies and outputs the possible alignments between them.
+
+The GraphMatcher leverages Graph Attention Network[2] in its neural network structure.
+The project leverages a new neighborhood aggregation algorithm, so it examines contribution of neighboring terms which have not been used in the previous matchers before.
+
+The project has been submitted to The 17th International Workshop on Ontology Matching's OAEI 2022 (ISWC-2022) for conference track and obtained the highest F1-measure in uncertain reference alignments among other experts participating to this challenge. Its system paper has been published, and it was invited to the poster presentation session.
+
+## Set up
+* 1.) install requirements
+``` pip install -r requirements.txt```
+
+* 2.) set the parameters in the config.ini
+````
+[General]
+dataset =               ------> name of a dataset e.g., conference.
+K =                     ------> the parameter for K fold cross-validation
+ontology_split =        ------> True/False
+max_false_examples =    ------>
+
+[Paths]
+dataset_folder =        ------> a path to the ontologies
+alignment_folder =      ------> a path to the reference alignments
+save_model_path =       ------> save the model to the path
+load_model_path =       ------> model path
+output_folder =         ------> The output folder for the alignments
+
+[Parameters]
+max_paths =             ------>
+max_pathlen =           ------> ( number of neighboring concepts' types: Equivalent class, subclass of(general to specific or specific to general(2))...
+[Hyperparameters] 
+
+lr =                    ------> learning rate
+num_epochs =            ------> number of epochs
+weight_decay =          ------> Weight decay
+batch_size =            ------> Batch Size (8/16/32)
+
+````
+
+* 3.) train the model 
+```python
+python src/train_model.py
+
+```
+* 4.) test the model
+```python
+python src/test_model.py ${source.rdf} ${target.rdf}
+```
+### Sample Alignment:
+```xml
+  <map>
+    <Cell>
+      <entity1 rdf:resource='http://conference#has_the_last_name'/>
+      <entity2 rdf:resource='http://confof#hasSurname'/>
+      <relation>=</relation>
+      <measure rdf:datatype='http://www.w3.org/2001/XMLSchema#float'>0.972</measure>
+    </Cell>
+  </map>
+```
+
+* 5.) evaluate the model with the MELT
+
+
+Note: The codes in train_model.py and test_model.py are partially based on the VeeAlign[2] project with the permission of its main author. I would like to thank the main author.
+
+## References:
+[1] 
+````
+@inproceedings{iyer-etal-2021-veealign,
+    title = "{V}ee{A}lign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment",
+    author = "Iyer, Vivek  and
+      Agarwal, Arvind  and
+      Kumar, Harshit",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.842",
+    doi = "10.18653/v1/2021.emnlp-main.842",
+    pages = "10780--10792",
+   }
+````
+[2]
+````
+@misc{https://doi.org/10.48550/arxiv.1710.10903,
+  title = {Graph Attention Networks},
+  author = {Veličković, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Liò, Pietro and Bengio, Yoshua},
+  keywords = {Machine Learning (stat.ML), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  url = {https://arxiv.org/abs/1710.10903},
+  publisher = {arXiv},
+  doi = {10.48550/ARXIV.1710.10903},
+  year = {2017},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+
+````