Entity matching with machine learning fuzzy logic
AssigneePalantir Technologies Inc.
InventorsElliot Hirsch, Johannes Beil, Lauren Brown, Nicolas Prettejohn, Paul Baseotto, Shaul Walter
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Abstract
A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.
Claims20
(canceled)
A computerized method, performed by a computing system having one or more hardware computer processors and one or more non-transitory computer readable storage device storing software instructions executable by the computing system to perform the computerized method comprising:
- determining, using a machine learning model, an overall match score for a first and second data record, wherein the machine learning model is configured to:
- determine one or more matching algorithm of a plurality of matching algorithms associated with individual properties that are part of both the first and second data records;
- and
- execute the determined one or more matching algorithms on the individual properties of the first and second data records;
- wherein a first matching algorithm of the plurality of matching algorithms is associated with a first individual property and a second matching algorithm of the plurality of matching algorithms is associated with a second individual property;
- and
- outputting the determined overall match score.
A computing system comprising:
- a hardware computer processor;
- and
- a non-transitory computer readable medium having software instructions stored thereon, the software instructions executable by the hardware computer processor to cause the computing system to perform operations comprising:
- determining, using a machine learning model, an overall match score for a first and second data record, wherein the machine learning model is configured to:
- determine one or more matching algorithm of a plurality of matching algorithms associated with individual properties that are part of both the first and second data records;
- and
- execute the determined one or more matching algorithms on the individual properties of the first and second data records;
- wherein a first matching algorithm of the plurality of matching algorithms is associated with a first individual property and a second matching algorithm of the plurality of matching algorithms is associated with a second individual property;
- and
- outputting the determined overall match score.
Dependencies are inferred from the claim text.