Quality Shapes Extraction (QSE)

QSE is a scalable shapes extraction tool which helps you extract validating shapes (SHACL) from large knowledge graphs.

Read the paper: https://www.vldb.org/pvldb/vol16/p1023-rabbani.pdf or visit our website for more details: https://relweb.cs.aau.dk/qse/

Instructions to extract SHACL shapes from your Knowledge Graph

We provide a JAR file to help user easily extract SHACL shapes given:

1. the input knowledge graph is in .nt format and
2. the config file contains correct parameters.

Set Config Params:

We already provide default parameters in the config.properties file, you only need to update the following parameters to get started:

  dataset_path=/dir_path/knowledge_graph.nt
  resources_path=/dir_path/qse/src/main/resources
  output_file_path=/dir_path/qse/Output/

Please replace the dir_path with respect to your directory. You can specify values of support and range (as pruning thresholds) in the config file as pairs.

Run Jar file:

The jar file is located in jar directory. Please execute the following command to run the jar:

java -jar -Xmx16g  build/libs/qse.jar config.properties &> output.logs

You can change the value for Xmx16g according to your machine's specification. It specifies the maximum memory usage by the JVM machine to run this jar.

Note: QSE requires Java to be installed on your system to run its Jar. You can install it by following these steps to install sdkman and execute the following commands to install the specified version of Java and Gradle.

    sdk list java
    sdk install java 17.0.2-open
    sdk use java 17.0.2-open
    sdk install gradle 7.4-rc-1

Output:

QSE will output SHACL shapes in the output_file_path directory along with classFrequency.csv file containing number of instances (nodes) of each class in the dataset and some other logs.

• The file with suffix _QSE_FULL_SHACL.ttl contains the full set of SHACL shapes using the configuration provided in config.properties file.
• The file with suffix _QSE_0.1_100_SHACL.ttl contains the set of SHACL shapes pruned using confidence 0.1 and support 100 using the configuration provided in config.properties file.

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Reproducibility

If you want to reproduce the results of the paper, please read VLDB_Reproducibility readme file.

Citing the work

Please cite us if you use the code in your project or publication

@article{DBLP:journals/pvldb/RabbaniLH23,
  author       = {Kashif Rabbani and
                  Matteo Lissandrini and
                  Katja Hose},
  title        = {Extraction of Validating Shapes from very large Knowledge Graphs},
  journal      = {Proc. {VLDB} Endow.},
  volume       = {16},
  number       = {5},
  pages        = {1023--1032},
  year         = {2023}
}

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License.