A computational block to solve entity alignment over textual attributes in a knowledge graph creation pipeline.

Related tags

Deep LearningEABlock
Overview

DOI

EABlock

How to apply?

  1. Create your config.ini file following the example provided in config.ini
  2. Choose one of the options below to run:

Run with Python3

pip install -r requirements.txt
python3 /PATH_TO_EABlock/Functions_Interpreter/run_translator.py /PATH_TO_YOUR_CONFIG_FILE/YOUR_CONFIG_FILE.ini

Directly use the docker image:

# move to docker-compose directory
cd docker

# run the docker instance
docker-compose up -d

# execution
docker exec -it EABlock python3 /EABlock/run_translator.py /source/config-test.ini

Build the docker image locally:

cd Functions_Interpreter

docker build -t sdmtib/EABlock:1.0 .

Reproducibility:

All the results of the three categories of the experimental studies that are performed in the paper to evaluate the performance of EABlock can be reproduced. The setup codes are available here and all data and intermediate datasets (outcome of codes) are accessible here. The details of the path to the datasets related to each experiment category is also provided in a seperated DATASETS txt files (example ).

Authors

Owner
Scientific Data Management Group
Scientific Data Management Group
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