Machine learning to tune probabilistic matching in entity resolution systems

Machine learning to tune probabilistic matching in entity resolution systems

Techniques for data evaluation are provided. A plurality of data records is received, and a first comparison vector is generated by comparing a first and a second data record of the plurality of data records, where the first comparison vector indicates differences between the first and second data r...

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Bibliographische Detailangaben
Hauptverfasser: O'Neill, Jr., James Albert, Khatibi, Mohammad, Seth, Abhishek, Naganna, Soma Shekar, Schumacher, Scott
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:Techniques for data evaluation are provided. A plurality of data records is received, and a first comparison vector is generated by comparing a first and a second data record of the plurality of data records, where the first comparison vector indicates differences between the first and second data records. A machine learning model is trained based at least in part on the first comparison vector. The plurality of data records is evaluated using the machine learning model, and at least two of the plurality of data records are linked based on the evaluation.