Automatic entity resolution with rules detection and generation system

A challenging training sample selection method comprising:

• consideration of record fields as indepenedent;
• selection of challenging training samples based on a number of multiple labels/classess that record field values, and/or link feature values are associated also termed ambiguity;
• proportional selection of challenging training samples based on a level of that record field values, and/or link feature values ambiguity;
• contribution of each linkage feature to the training sample is determined by its relative level of ambiguity;
• contribution of a linkage feature value within a linkage feature is determined by its relative level of ambiguty;
• contribution of value pairs associated with the same linkage feature is determined by its relative level of ambiguity;
• and
• use of that record field values from positive labels to optimize the determination of record field values, and/or link feature values ambiguty.

A non-transitory computer readable information storage media having stored thereon instructions, that when executed by a processor perform a challenging training sample selection method comprising:

• consideration of record fields as indepenedent;
• selection of challenging training samples based on a number of multiple labels/classess that record field values, and/or link feature values are associated also termed ambiguity;
• proportional selection of challenging training samples based on a level of that record field values, and/or link feature values ambiguity;
• contribution of each linkage feature to the training sample is determined by its relative level of ambiguity;
• contribution of a linkage feature value within a linkage feature is determined by its relative level of ambiguty;
• contribution of value pairs associated with the same linkage feature is determined by its relative level of ambiguity;
• and
• use of that record field values from positive labels to optimize the determination of record field values, and/or link feature values ambiguty.

A challenging training sample selection system comprising:

• a processor;
• an I/O interface;
• and
• storage, the storage having stored thereon instructions, that when executed by the processor, cause to be performed:
• consideration of record fields as indepenedent;
• selection of challenging training samples based on a number of multiple labels/classess that record field values, and/or link feature values are associated also termed ambiguity;
• proportional selection of challenging training samples based on a level of that record field values, and/or link feature values ambiguity;
• contribution of each linkage feature to the training sample is determined by its relative level of ambiguity;
• contribution of a linkage feature value within a linkage feature is determined by its relative level of ambiguty;
• contribution of value pairs associated with the same linkage feature is determined by its relative level of ambiguity;
• and
• use of that record field values from positive labels to optimize the determination of record field values, and/or link feature values ambiguty.