Approaches to vectorize Sparql queries and their metadata for Machine Learning tasks.
- Rhassan code for algebra queries representation.
- Rhassan code for graph pattern queries representation based in kmedoids.
- Queries representations using sets of features for DeepSet architecture(working on..)
For run use that
System.out.println("Try with some of this parameters:");
java -jar file.jar kmedoids /path/to/input.csv /path/to/output.csv /path/to/ids_time.csv #-of-centroids
java -jar file.jar edit-distance /path/to/input.csv /path/to/output.csv /path/to/prefixes #-of-cores
Last number(4) is the number of cores or proccess to run in paralell.
java -jar file.jar deepset-features /path/to/input.csv /path/to/output.csv tables,joins,predicates /path/to/prefixes [--cores=numOfCores] [--length=numOfTuples] [--output-delimiter=symbolToDelimitColumns]
Execute to compile:
mvn clean package
In the generated target you will find a graph-edit-distance-1.0-SNAPSHOT.jar