INFORMATION MATCHING APPARATUS, METHOD OF MATCHING INFORMATION, AND COMPUTER READABLE STORAGE MEDIUM HAVING STORED INFORMATION MATCHING PROGRAM

• US 20120197826A1
• Filed: 11/30/2011
• Published: 08/02/2012
• Est. Priority Date: 01/28/2011
• Status: Abandoned Application

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: MINENO, Kazuo
• Application Number:
  US13/307,050
• Publication Number:
  US 20120197826A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/086 using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

The information matching apparatus includes: a training data rule setting unit that sets rules defining conditions for a training data of a positive example that is a pair of the records to be judged to be identical and a training data of a negative example that is a pair of the records to be judged to be non-identical; and a training data generating unit that, for the record of a matching source, generates a training data of the positive example by searching for the records of a matching target by using a positive example rule that is a rule defining conditions for the training data of the positive example, and generates a training data of the negative example by searching for the records of the matching target by using a negative example rule that is a rule defining conditions for the training data of the negative example.

Claims

1. An information matching apparatus comprising:

a processor; and

a memory, wherein the processor executes;

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

generating a training data, for the record of a matching source, by generating the supervised data of the positive example by searching for the records of a matching target by using a positive example rule that is set at the setting and is a rule defining conditions for the supervised data of the positive example, and by generating the supervised data of the negative example by searching for the records of the matching target by using a negative example rule that is set at the setting and is a rule defining conditions for the supervised data of the negative example.

2. The information matching apparatus according to claim 1, wherein the setting rules that includes one of a condition that all the values corresponding to matching target items between the records match each other as a condition for the supervised data of the positive example and a condition that all the values corresponding to the matching target items between the records never match each other as a condition for the supervised data of the negative example.

3. The information matching apparatus according to claim 1, wherein the generating the training data that determines whether the generated supervised data of the positive example does not coincide with the negative example rule, determines whether the generated supervised data of the negative example does not coincide with the positive example rule, and, in a case where the supervised data coincides with the rule, removes the supervised data that coincides with the rule.

4. An information matching apparatus comprising:

a processor; and

a memory, wherein the processor executes;

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

verifying a training data by acquiring the supervised data of the positive example or the negative example and by determining whether the acquired supervised data does not coincide with the rule that is set at the setting and is a rule of a classification that is opposite to a classification of the positive example or the negative example included in the supervised data.

5. The information matching apparatus according to claim 4, wherein verifying the training data that additionally determines whether the supervised data coincides with the rule of the classification of the positive example or the negative example that is included in the supervised data.

6. An information matching apparatus comprising:

a processor; and

a memory, wherein the processor executes;

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

judging a name identification result by judging a classification of identity, non-identity, or non-judgment based on the rules set at setting for the pair of the records that is judged to be undeterminable as a result of the judgment.

7. A method of matching information performed by an information matching apparatus, the method comprising:

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

generating the supervised data of the positive example for the record of a matching source by searching for the records of a matching target by using a positive example rule that is set and is a rule defining conditions for the supervised data of the positive example, and generating the supervised data of the negative example by searching for the records of the matching target by using a negative example rule that is set and is a rule defining conditions for the supervised data of the negative example.

8. A method of matching information performed by an information matching apparatus, the method comprising:

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

acquiring the supervised data of the positive example or the negative example and determining whether the acquired supervised data does not coincide with the rule that is set and is a rule of a classification that is opposite to a classification of the positive example or the negative example included in the supervised data.

9. A method of matching information performed by an information matching apparatus, the method comprising:

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

judging a classification of identity, non-identity, or non-judgment based on the set rules for the pair of the records that is judged to be undeterminable as a result of the judgment.

10. A non-transitory computer readable storage medium having stored therein an information matching program causing an information matching apparatus, to execute a process comprising:

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

generating the supervised data of the positive example for the record of a matching source by searching for the records of a matching target by using a positive example rule that is set and is a rule defining conditions for the supervised data of the positive example, and generating the supervised data of the negative example by searching for the records of the matching target by using a negative example rule that is set and is a rule defining conditions for the supervised data of the negative example.

11. A non-transitory computer readable storage medium having stored therein an information matching program causing an information matching apparatus, to execute a process comprising:

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

acquiring the supervised data of the positive example or the negative example and determining whether the acquired supervised data does not coincide with the rule that is set and is a rule of a classification that is opposite to a classification of the positive example or the negative example included in the supervised data.

12. A non-transitory computer readable storage medium having stored therein an information matching program causing an information matching apparatus, to execute a process comprising:

setting rules defining conditions for supervised data of a positive example that is a pair of the records to be judged to be identical and supervised data of a negative example that is a pair of the records to be judged to be non-identical as the supervised data used for learning judgment criteria used for the judgment through supervised learning; and

judging a classification of identity, non-identity, or non-judgment based on the set rules for the pair of the records that is judged to be undeterminable as a result of the judgment.