MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rules
Introduction
This repository contains the code for our paper:
Title: MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rules.
Authors: Huang Fang, Yang Liu, Yunfeng Cai, Mingming Sun.
Affiliation: Baidu Research, Cognitive Computing Lab (CCL).
Quick start
Open Julia in terminal under this folder and go to the package REPL by pressing ], type activate . to activate the package. Then go back to Julia REPL by pressing the backslash.
The "smokers and friends" toy example:
Load packages:
push!(LOAD_PATH, pwd())
using Revise, mln4kb, Printf
MLN inference:
factFile = "./examples/smoke/facts.txt"
ruleFile = "./examples/smoke/rules.txt"
mln = MLN(factFile, ruleFile);
PrepareMLN!(mln)
# Extract facts
ExtractFacts(mln.kb, "Friends")
# Inference
objList, numViolatedList = WalkSAT!(mln, maxIter=Int(1e2), warmupPeriod=Int(1e2))
Weight learning:
iterate = zeros( Float64, length( mln.rules ) )
lr = 1e-1
optimizer = AdaGradOptimizer(lr, iterate)
OptimizePseudoLogLikelihood!( mln, optimizer, numNegativeSamples=1, maxIter=Int(1e2), resetMLN=false )
More test examples can be found in ./examples/run_examples.jl.
Citation
If you find this project helpful, please cite the code with the following bibtex.
@inproceedings{fang2023mln4kb,
title={MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rules},
author={Huang Fang and Yang Liu and Yunfeng Cai and Mingming Sun},
booktitle={The International World Wide Web Conference 2023},
year={2023}
}
Contact
Please feel free to send your comments and contact us by fangazq877@gmail.com. We are considering to develop a C/C++ version of MLN4KB (with multi-CPU parallelization), please let us know if you find MLN4KB.jl is still too slow for your application.