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Stacked Threshold-based Entity Matching (STEM) allows to run several instances of unsupervised matchers and use their predictions as a feature vector for an SVM classifier. The experiments have been conducted using two different base classifier (Duke, Silk) on three datasets (FEIII, 3cixty and DOREMUS).
-Put your configuration file into config/your_experiment -Put your data sets to match into data/your_experiment -Put your gold standard file into data/your_experiment/gs
###DUKE
cd STEM/src
-FEIII python STEM.py -i ../config/FEIII2016/FFIEC_SEC.xml -N 10 -a 0.2 -g ../data/FEIII2016/gs/FFIEC-SEC-GroundTruth.csv -s duke
-3cixty python STEM.py -i ../config/3cixty/config_duke_3cixty_nice_events.xml -g ../data/3cixty/gs/gs_3cixty_nice_events.csv -N 10 -a 0.2 -s duke
-DOREMUS python STEM.py -i ../config/DOREMUS/Config1_duke.xml -N 10 -a 0.2 -g ../data/DOREMUS/Doremus/4-heterogeneities/doremus_gs.csv -s duke
###SILK
cd STEM/src
-FEIII python STEM.py -i ../config/FEIII2016/FFIEC_SEC_silk.xml -N 10 -a 0.2 -g ../data/FEIII2016/gs/FFIEC-SEC-GroundTruth.csv -s silk
-3cixty python STEM.py -i ../config/3cixty/config_duke_3cixty_nice_events.xml -g ../data/3cixty/gs/gs_3cixty_nice_events.csv -N 10 -a 0.2 -s silk
-DOREMUS python STEM.py -i ../config/DOREMUS/Config1.xml -N 10 -a 0.2 -g ../data/DOREMUS/4-heterogeneities/gs/doremus_gs.csv -s silk