AUTOMATED MANAGEMENT OF NATURAL LANGUAGE QUERIES IN ENTERPRISE BUSINESS INTELLIGENCE ANALYTICS

• US 20170060868A1
• Filed: 08/28/2015
• Published: 03/02/2017
• Est. Priority Date: 08/28/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Rais Ghasem, Mohsen, Truong, Kenny, Vierich, Ralf
• Granted Patent: US 9,984,116 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/243 Natural language query formulation
• CPC Class Codes: G06F 16/24522 Translation of natural language queries to structured queries
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Techniques are described for automated management of natural language queries of enterprise data. In one example, a method includes performing natural language processing and semantic processing on a natural language query to identify data sets relevant to the natural language query. The method further includes grouping the data sets into one or more query domains based at least in part on one or more relationships among the data sets. The method further includes prioritizing the query domain sets. The method further includes loading one or more of the query domain sets in an order based on the prioritizing of the query domain sets.

Claims

1. A method comprising:

performing, with one or more processing devices, natural language processing and semantic processing on a natural language query to identify data sets relevant to the natural language query;

grouping, with the one or more processing devices, the data sets into one or more query domains based at least in part on one or more relationships among the data sets;

prioritizing, with the one or more processing devices, the query domain sets; and

loading, with the one or more processing devices, one or more of the query domain sets in an order based on the prioritizing of the query domain sets.

2. The method of claim 1, further comprising evaluating sizes of the one or more query domains, wherein the filtering the one or more of the query domains is based at least in part on the evaluated sizes of the one or more query domains.

3. The method of claim 1, further comprising filtering the one or more of the query domains into a plurality of filtered query domain sets prior to the prioritizing of the filtered query domain sets.

4. The method of claim 1, further comprising presenting, in a user interface, one or more user menus for user-enabled filtering of the query domain sets.

5. The method of claim 1, further comprising presenting, in a user interface, an indication of a size of the query domain based on the natural language query.

6. The method of claim 1, further comprising presenting, in a user interface, an indication of a size of the query domain based on filters applied to the natural language query.

7. The method of claim 1, further comprising presenting, in a user interface, an indication of a load time of the query domain based on the natural language query.

8. The method of claim 1, further comprising presenting, in a user interface, an indication of a load time of the query domain based on filters applied to the natural language query.

9. The method of claim 1, wherein grouping the data sets into one or more query domains based at least in part on one or more relationships among the data sets comprises:

identifying two or more data sets with lexical or ontological correlation with parsed query terms from the natural language query.

10. The method of claim 1, wherein grouping the data sets into one or more query domains based at least in part on one or more relationships among the data sets comprises:

identifying a data set that has one-to-many cardinality with one or more other data sets, and no many-to-one cardinality with any data set.

11. The method of claim 10, wherein the identified data set that has one-to-many cardinality with one or more other data sets and no many-to-one cardinality with any data set is a fact table, and wherein grouping the data sets into one or more query domains based at least in part on one or more relationships among the data sets further comprises:

identifying one or more data sets having a direct many-to-one cardinality relationship with the fact table.

12. The method of claim 1, wherein grouping the data sets into one or more query domains based at least in part on one or more relationships among the data sets comprises:

identifying a data set that has one-to-many cardinality with one or more other data sets, and no many-to-one cardinality with any data set.

14. The computer program product of claim 1, wherein the program code is further executable by the computing device to evaluate sizes of the one or more query domains, wherein the filtering the one or more of the query domains is based at least in part on the evaluated sizes of the one or more query domains.

15. The computer program product of claim 1, wherein the program code is further executable by the computing device to filter the one or more of the query domains into a plurality of filtered query domain sets prior to the prioritizing of the filtered query domain sets.

16. The computer program product of claim 1, wherein the program code is further executable by the computing device to present, in a user interface, one or more user menus for user-enabled filtering of the query domain sets.

13. A computer program product comprising a computer-readable storage medium having program code embodied therewith, the program code executable by a computing device to:

perform natural language processing and semantic processing on a natural language query to identify data sets relevant to the natural language query;

group the data sets into one or more query domains based at least in part on one or more relationships among the data sets;

prioritize the query domain sets; and

load one or more of the query domain sets in an order based on the prioritizing of the query domain sets.

17. A computer system comprising:

one or more processors, one or more computer-readable memories, and one or more computer-readable, tangible storage devices;

program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform natural language processing and semantic processing on a natural language query to identify data sets relevant to the natural language query;

program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to group the data sets into one or more query domains based at least in part on one or more relationships among the data sets;

program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to prioritize the query domain sets; and

program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to load one or more of the query domain sets in an order based on the prioritizing of the query domain sets.

18. The computer system of claim 17, further comprising program instructions to evaluate sizes of the one or more query domains, wherein the filtering the one or more of the query domains is based at least in part on the evaluated sizes of the one or more query domains.

19. The computer system of claim 17, further comprising program instructions to filter the one or more of the query domains into a plurality of filtered query domain sets prior to the prioritizing of the filtered query domain sets.

20. The computer system of claim 17, further comprising program instructions to present, in a user interface, one or more user menus for user-enabled filtering of the query domain sets.