1. About DisLocKG
2. Repository Description
3. Getting started with DisLocKG Docker container
4. Uploading the knowledge graph into the triplestore
5. Querying the graph using the SPARQL endpoint
6. Contact
7. Acknowledgements
8. License

DisLocKG is a knowledge graph that presents data regarding dislocation in crystalline materials, arranged in a semantic network. DisLocKG also preserves the provenance information of the data, which includes the creator's data, software used, and software version utilized to produce the data. Currently, we have generated approximately 2.2 million triples that are saved as RDF files as of 01/08/2023.

• You can find raw data here.
• All data files are in the HDF5 format (.h5).
• To generate the RDF graph from the given raw data, we developed a python script using the RDFLib python library and execute it with python map_data.py.
• You can find a set of competence questions (CQs) along with SPARQL queries here.

To begin, you must set up and execute a Virtuoso Open Source instance that includes a SPARQL endpoint. If you plan to run the DisLocKG Docker container, ensure that Docker is installed on your device and follow the instructions provided below:

1. Clone the repository

git clone https://github.com/Materials-Data-Science-and-Informatics/DisLocKG

2. Change the directory into the DisLocKG repository

cd DisLocKG

3. Build and start the containers with Docker compose

docker compose up --build

or docker-run

docker run  -d --name DisLocKG --env DBA_PASSWORD=1234567890 --env SPARQL_UPDATE=false --env DEFAULT_GRAPH=http://localhost:8899/dislockg --publish 1111:1111  --publish  8899:8890 openlink/virtuoso-opensource-7:latest

4. Wait for the Virtuoso instance to start up (you can check the logs with docker-compose logs virtuoso

1. First, download the knowledge graph from here.
2. While the Virtuoso container is running (in the background), go to localhost:8899/conductor on your browser. You will see the page as seen in the figure below:

3. On the upper left, there is a login box, please log in with a credential: Account: dba and Password: 1234567890:

4. Once you have successfully logged in, follow these steps:
   1. Navigate to the Linked Data menu.
   2. Select Quad Store Upload.
   3. Locate DisLocKG-01.08.2023.ttl in your local machine.
   4. Enter the name of the graph IRI as http:localhost:8890/DisLocKG.
   5. Click on the upload button.

NOTE: It may take time, approx. 3-5 minutes, to upload the file.

5. If the uploading process is successfully done, you may see the site in the following

• Once you have successfully uploaded the knowledge graph, you can access DisLocKG by querying it via the SPARQL endpoint:

  localhost:8899/sparql
  

• All CQs are available in CQs

You may contact the author of DisLocKG via a.ihsan@fz-juelich.de

• European Research Council through the ERC Grant Agreement No. 759419 MuDiLingo (”A Multiscale Dislocation Language for Data-Driven Materials Science”)
• Helmholtz Metadata Collaboration (HMC) within the Hub Information at the Forschungszentrum Jülich.

The code is licensed under the MIT license.