Cognitive visual and ontological mapping of tabular data based on a distribution of a measure of interest

Patent metadata

Field	Value
Publication number	US-10977294-B2
Application number	US-201816224580-A
Country	US
Kind code	B2
Filing date	Dec 18, 2018
Priority date	Dec 18, 2018
Publication date	Apr 13, 2021
Grant date	Apr 13, 2021

Abstract

One embodiment provides a computer program product for cognitive visual and ontological mapping of tabular data. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to receive a data query and ontology information relating to the data query, receive context information relating to a display device, determine a measure of interest for the data query and one or more constraints for the measure of interest, determine saliency features based on the context information, and generate a user interface comprising query results corresponding to the data query arranged in a dynamic tabular format for display on the display device. The dynamic tabular format is based on the ontology information, the measure of interest, the one or more constraints for the measure of interest, and the saliency features.

First claim

The invention claimed is:

1. A computer program product for cognitive visual and ontological mapping of tabular data, 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:
receive a data query and ontology information relating to the data query, wherein the ontology information comprises a hierarchy of category descriptions;
receive context information relating to a display device;
determine a measure of interest for the data query and one or more constraints for the measure of interest;
determine saliency features based on the context information; and
arrange query results corresponding to the data query in a dynamic tabular format for display as a user interface on the display device, wherein the dynamic tabular format is based on the ontology information, the measure of interest, the one or more constraints for the measure of interest, and the saliency features, and wherein the processor arranges the query results by shifting a portion of the query results from a first category description of the hierarchy to a second category description of the hierarchy that is broader than the first category description of the hierarchy.

2. The computer program product of claim 1 , wherein the saliency features relate to at least one of layout and typographic emphasis of the user interface.

3. The computer program product of claim 2 , wherein the saliency features comprise one or more of the following: text size, text placement, text color, opacity, text emphasis, or indentation.

4. The computer program product of claim 1 , wherein the program instructions are further executable by the processor to cause the processor to:
for a query result, map at least one saliency feature of the saliency features to a distributional share of the measure of interest for the query result, and display the query result on the display device in accordance with the at least one saliency feature mapped.

5. The computer program product of claim 1 , wherein the measure of interest and the one or more constraints for the measure of interest are user-specified and received via an electronic device.

6. The computer program product of claim 1 , wherein the program instructions are further executable by the processor to cause the processor to:
apply a machine learning model to determine the measure of interest and the one or more constraints for the measure of interest, wherein the machine learning model is trained on one of an electronic device or a server device.

7. The computer program product of claim 1 , wherein the ontology information comprises a pre-specified ordering of importance of the one or more descriptive hierarchies.

8. The computer program product of claim 7 , wherein the program instructions are further executable by the processor to cause the processor to:
organize the hierarchy of category descriptions based on the pre-specified ordering of importance;
aggregate the query results based on the measure of interest and the organized hierarchy; and
re-arrange the aggregated query results based on the one or more constraints for the measure of interest.

9. The computer program product of claim 8 , wherein the program instructions are further executable by the processor to cause the processor to:
perform an ontological shift involving shifting a portion of the query results from a first category description of the organized hierarchy to a second category description of the organized hierarchy that is broader than the first category description of the organized hierarchy.

10. The computer program product of claim 9 , wherein the program instructions are further executable by the processor to cause the processor to:
for each query result involved in the ontological shift, utilize at least one saliency feature of the saliency features as at least one signifier of the ontological shift, and display the query result on the display device in accordance with the at least one signifier.

11. The computer program product of claim 9 , wherein the one or more constraints for the measure of interest comprise one or more of the following: a minimum range for the measure of interest for performing the ontological shift, a maximum range for the measure of interest for performing the ontological shift, a lower-bound threshold for the measure of interest for performing the ontological shift, or an upper-bound threshold for the measure of interest for performing the ontological shift.

12. A system for cognitive visual and ontological mapping of tabular data, comprising:
at least one processor; and
a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including:
receiving a data query and ontology information relating to the data query, wherein the ontology information comprises a hierarchy of category descriptions;
receiving context information relating to a display device;
determining a measure of interest for the data query and one or more constraints for the measure of interest;
determining saliency features based on the context information; and
arranging query results corresponding to the data query in a dynamic tabular format for display as a user interface on the display device, wherein the dynamic tabular format is based on the ontology information, the measure of interest, the one or more constraints for the measure of interest, and the saliency features, and wherein the at least one processor arranges the query results by shifting a portion of the query results from a first category description of the hierarchy to a second category description of the hierarchy that is broader than the first category description of the hierarchy.

13. The system of claim 12 , wherein generating a user interface comprising query results corresponding to the data query arranged in a dynamic tabular format for display on the display device comprises:
for a query result, mapping at least one saliency feature of the saliency features to a distributional share of the measure of interest for the query result, and displaying the query result on the display device in accordance with the at least one saliency feature mapped.

14. The system of claim 12 , wherein the measure of interest and the one or more constraints for the measure of interest are user-specified and received via an electronic device.

15. The system of claim 12 , wherein determining a measure of interest for the data query and one or more constraints for the measure of interest comprises applying a machine learning model, and the machine learning model is trained on one of an electronic device or a server device.

16. The system of claim 12 , wherein the ontology information comprises a pre-specified ordering of importance of the one or more descriptive hierarchies.

17. The system of claim 16 , wherein the operations further comprise:
organizing the hierarchy of category descriptions based on the pre-specified ordering of importance;
aggregating the query results based on the measure of interest and the organized hierarchy; and
re-arranging the aggregated query results based on the one or more constraints for the measure of interest.

18. The system of claim 17 , wherein the operations further comprise:
performing an ontological shift involving shifting a portion of the query results from a first category description of the organized hierarchy to a second category

Assignees

• IBM

Inventors

• Arar Raphael
• Strong Jr Hovey R
• Ren Guangjie
• Chowdhary Pawan

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F16/3334

  Selection or weighting of terms from queries, including natural language queries · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06F16/367 Primary

  Ontology · CPC title