Processing un-typed triple store data

• US 10,303,790 B2
• Filed: 06/08/2016
• Issued: 05/28/2019
• Est. Priority Date: 06/08/2016
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bishop, Timothy A., Boxwell, Stephen A., Brumfield, Benjamin L., Vernier, Stanley J.
• Primary Examiner(s): Khong, Alexander
• Application Number:
  US15/176,198
• Publication Number:
  US 20170357690A1
• Time in Patent Office: 1,084 Days
• CPC Class Codes: G06F 16/242 Query formulation
• CPC Class Codes: G06F 16/24534 Query rewriting; Transformation
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems

Abstract

A method, executed by a computer, includes receiving a triple comprising a predicate, a first entity and an un-typed second entity, scanning a collection of triples for triples that match the predicate of the triple to produce similar triples, determining frequency information for second entity types referenced by the similar triples, and processing the un-typed second entity using the frequency information. A computer system and computer program product corresponding to the method are also disclosed herein.

Claims

1. A method, executed by one or more processors, the method comprising:

in response to receiving a triple comprising a subject, a predicate, and an un-typed object, wherein the un-typed object is a string literal, scanning a database storing a collection of triples for triples that match the predicate of the received triple to identify similar triples;

responsive to identifying an object of each similar triple, for each non-literal object identified, scanning the database storing the collection of triples for typing triples that (i) have a subject that matches a respective non-literal object and (ii) include a type predicate, thereby classifying objects of the typing triples as candidate data types for the un-typed object;

identifying the candidate data types for the untyped-object;

determining frequency information for each candidate data type based on a ratio of a count of the candidate data types to a count of the similar triples;

associating the un-typed object with one or more candidate data types based on the frequency information; and

processing the un-typed object based on the frequency information and the one or more candidate data types.

2. The method of claim 1, wherein the similar triples also match a subject type of the received triple.

3. The method of claim 1, further comprising determining a confidence score for a particular data type for the received triple from the frequency information.

4. The method of claim 1, wherein the frequency information comprises a count for one or more objects referenced by the similar triples.

5. The method of claim 1, wherein the frequency information comprises a percentage for one or more object types referenced by the similar triples.

6. The method of claim 1, wherein processing the un-typed object comprises candidate answer generation.

7. The method of claim 1, wherein processing the un-typed object comprises candidate answer evaluation.

8. A computer system comprising:

one or more computer processors;

one or more computer readable storage devices and program instructions stored on the one or more computer readable storage devices, the program instructions comprising instructions to perform;

in response to receiving a triple comprising a subject, a predicate, and an un-typed object, wherein the un-typed object is a string literal, scanning a database storing a collection of triples for triples that match the predicate of the received triple to identify similar triples;

responsive to identifying an object of each similar triple, for each non-literal object identified, scanning the database storing the collection of triples for typing triples that (i) have a subject that matches a respective non-literal object and (ii) include a type predicate, thereby classifying objects of the typing triples as candidate data types for the un-typed object;

identifying the candidate data types for the untyped-object;

determining frequency information for each candidate data type based on a ratio of a count of the candidate data types to a count of the similar triples;

associating the un-typed object with one or more candidate data types based on the frequency information; and

processing the un-typed object based on the frequency information and the one or more candidate data types.

9. The computer system of claim 8, wherein the similar triples also match a subject type of the received triple.

10. The computer system of claim 8, further comprising determining a confidence score for a particular data type for the received triple from the frequency information.

11. The computer system of claim 8, wherein the frequency information comprises a count for one or more objects referenced by the similar triples.

12. The computer system of claim 8, wherein the frequency information comprises a percentage for one or more object types referenced by the similar triples.

13. The computer system of claim 8, wherein processing the un-typed object comprises candidate answer generation.

14. The computer system of claim 8, wherein processing the un-typed object comprises candidate answer evaluation.

15. A computer program product comprising:

one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising instructions to perform;

in response to receiving a triple comprising a subject, a predicate, and an un-typed object, wherein the un-typed object is a string literal, scanning a database storing a collection of triples for triples that match the predicate of the received triple to identify similar triples;

responsive to identifying an object of each similar triple, for each non-literal object identified, scanning the database storing the collection of triples for typing triples that (i) have a subject that matches a respective non-literal object and (ii) include a type predicate, thereby classifying objects of the typing triples as candidate data types for the un-typed object;

identifying the candidate data types for the untyped-object;

determining frequency information for each candidate data type based on a ratio of a count of the candidate data types to a count of the similar triples;

associating the un-typed object with one or more candidate data types based on the frequency information; and

processing the un-typed object based on the frequency information and the one or more candidate data types.

16. The computer program product of claim 15, wherein the similar triples also match a subject type of the received triple.

17. The computer program product of claim 15, further comprising determining a confidence score for a particular data type for the received triple from the frequency information.

18. The computer program product of claim 15, wherein the frequency information comprises a count for one or more objects referenced by the similar triples.

19. The computer program product of claim 15, wherein the frequency information comprises a percentage for one or more object types referenced by the similar triples.

20. The computer program product of claim 15, wherein processing the un-typed object comprises candidate answer generation or candidate answer evaluation.