Facilitating data-driven mapping discovery

Patent metadata

Field	Value
Publication number	US-11100425-B2
Application number	US-201715798493-A
Country	US
Kind code	B2
Filing date	Oct 31, 2017
Priority date	Oct 31, 2017
Publication date	Aug 24, 2021
Grant date	Aug 24, 2021

Abstract

Systems, computer-implemented methods and/or computer program products that facilitate automatically mapping different data types are provided. In one embodiment, a computer-implemented method comprises: constructing, by a system operatively coupled to a processor, an index from one or more classifier models for one or more data types; scoring and ranking, by the system, one or more candidate pairs for the one or more data types based on confidence score; and analyzing, by the system, how the one or more candidate pairs are scored and automatically generating the one or more classifier models used to construct the index.

First claim

What is claimed is:

1. A system, comprising:
a memory;
a processor, operably coupled to the memory, and the memory, wherein the processor:
constructs an index from one or more classifier models for one or more data types;
scores and ranks one or more candidate pairs for the one or more data types based on confidence score;
searches the index for the one or more candidate pairs from the one or more data types;
analyzes how the one or more candidate pairs are scored and automatically generates the one or more classifier models used to construct the index; and
selects the one or more candidate pairs to train the one or more classifier models based on an analysis of how the one or more candidate pairs are scored by comparing different confidence scores from the one or more classifier models of the one or more data types.

2. The system of claim 1 , wherein the processor collects data used to generate the one or more classifier models.

3. The system of claim 1 , wherein the processor automatically generates one or more maps used to automatically generate the one or more classifier models.

4. The system of claim 1 , wherein the processor produces priority levels for the one or more candidate pairs based on a determination that the confidence score is equal to or greater than a defined threshold.

5. The system of claim 4 , wherein the processor constructs a new classifier model if the confidence score of the one or more candidate pairs for the one or more data types is below the defined threshold.

6. The system of claim 1 , wherein the processor modifies one or more scoring parameters and the defined threshold if the one or more candidate pairs selected to train the one or more classifier models are few.

7. A computer-implemented method, comprising:
constructing, by a system operatively coupled to a processor, an index from one or more classifier models for one or more data types;
scoring and ranking, by the system, one or more candidate pairs for the one or more data types based on confidence score;
searching, by the system, the index for the one or more candidate pairs from the one or more data types;
analyzing, by the system, how the one or more candidate pairs are scored and automatically generating the one or more classifier models used to construct the index; and
selecting, by the system, the one or more candidate pairs to train the one or more classifier models based on an analysis of how the one or more candidate pairs are scored by comparing different confidence scores from the one or more classifier models of the one or more data types.

8. The computer-implemented method of claim 7 , further comprising using the machine learning component to automatically generate one or more maps used to automatically generate the one or more classifier models.

9. The computer-implemented method of claim 7 , further comprising using an output component to produce priority levels for the one or more candidate pairs based on a determination that the confidence score is equal to or greater than a defined threshold.

10. The computer-implemented method of claim 9 , further comprising using the machine learning component to construct a new classifier model if the confidence score of the one or more candidate pairs for the one or more data types is below the defined threshold.

11. A computer program product for facilitating automatically mapping different data types, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
construct an index from one or more classifier models for one or more data type;
score and rank one or more candidate pairs for the one or more data types based on confidence score;
search the index for the one or more candidate pairs from the one or more data types;
analyze how the one or more candidate pairs are scored and automatically generate the one or more classifier models used to construct the index; and
select the one or more candidate pairs to train the one or more classifier models based on an analysis of how the one or more candidate pairs are scored by comparing different confidence scores from the one or more classifier models of the one or more data types.

12. The computer program product of claim 11 , wherein the program instructions are further executable to cause the processor to:
automatically generate one or more maps used to automatically generate the one or more classifier models.

13. The computer program product of claim 11 , wherein the program instructions are further executable to cause the processor to:
produce priority levels for the one or more candidate pairs based on a determination that the confidence score is equal to or greater than a defined threshold.

14. The computer program product of claim 13 , wherein the program instructions are further executable to cause the processor to:
construct a new classifier model if the confidence score of the one or more candidate pairs for the one or more data types is below the defined threshold.

Assignees

• IBM

Inventors

• Jiang Jian Min
• Liu Pei Ni
• Ni Yuan
• Sun Wen
• Xie Guo Tong
• Xu Jing Min

Classifications

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06F16/901 Primary

  Indexing; Data structures therefor; Storage structures (for retrieval from the web G06F16/951) · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F16/906

  Clustering; Classification · CPC title