Yongquan-He/ASER_AMIE

A Python pipeline for using AMIE+ to mine logic rules and instantiate new facts.

ASER_AMIE

A Python pipeline for using AMIE+ to mine logic rules and instantiate new facts. Initially designed for mining new relations for ASER

Settings and Dependencies:

• Python 3.7
• AMIE+

Usage:

• Show help message and descriptions of arguments

python pipeline.py -h

• Run the whole pipeline (Only for ASER Knowledge Graph):

python pipeline.py -wp --row_triples /path/to/row_triples.tsv --db_path DB_PATH /path/to/KG.db --amie_plus_path /path/to/AMIE+.jar --new_prediction_path /path/to/new_prediction.tsv

With this command, the pipeline will first extract RDF triples from ASER format database into .tsv file. Then it will run AMIE+ on the .tsv file to mine all logical rules within preset threshold. Finally it will instantiate new RDF facts by grounding the mined rules to orignal triples.

• Run pipeline for other knowledge base:

  • Mine logical rules with AMIE+:

  python pipeline.py -m --row --row_triples /path/to/row_triples.tsv --amie_plus_path /path/to/AMIE+.jar 

  The mined rules will be sorted according to the PCA and STD confidence repectively and saved in "pca_sorted_rule.tsv" and "std_sorted_rule.tsv" in the module directory.

  • Predict/Instantiate new facts with mined/provided rules:

  python pipeline.py -p --rule_path /path/to/rule_you_provide.tsv --row_triples /path/to/row_triples.tsv ----new_prediction_path /path/to/new_prediction.tsv