PROBABILITY MAPPING MODEL FOR LOCATION OF NATURAL RESOURCES

• US 20150332157A1
• Filed: 05/15/2014
• Published: 11/19/2015
• Est. Priority Date: 05/15/2014
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Baughman, Aaron K., Harrison, Thomas B., O'Connell, Brian M.
• Granted Patent: US 10,318,552 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

Claims

1. A method for locating a natural resource, the method comprising:

generating, by a computer processor, a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource;

generating, by the computer processor, a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources;

determining, by the computer processor, a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data;

combining, by the computer processor, one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and

determining, by the computer processor, a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses associated with the location of a natural resource.

2. The method of claim 1, wherein the topic-based dataset generated from the plurality of information sources includes multimedia data.

3. The method of claim 1, wherein the topic-based dataset generated from the plurality of information sources further includes substantially real-time data.

4. The method of claim 1, further comprising:

transforming, by the computer processor, the topic-based dataset into a summarized format output, based, at least in part, on processing by one or more analytic engines.

5. The method of claim 1, wherein the topic-based dataset is weighted based on respective beta distributions of the topic-based data.

6. The method of claim 1, further comprising:

combining, by the computer processor, hypotheses corresponding to the one or more pairs of clusters that are combined.

7. The method of claim 1, further comprising:

generating, by the computer processor, one or more feature vectors from the one or more features of the plurality of clusters;

generating, by the computer processor, a plurality of cluster spaces, which include the one or more pairs of clusters of the plurality of clusters that are combined, wherein each cluster space is based on a disparate threshold of similarity;

determining, by the computer processor, a cluster space of the plurality of cluster spaces that is favorable, based on a score of the cluster space and the disparate threshold of similarity; and

generating, by the computer processor, one or more proximity matrices from the one or more features vectors, based on the cluster space that is favorable.

8. The method of claim 7, wherein determining the cluster space that is favorable further comprises:

determining, by the computer processor, a limit of combining clusters of the plurality of clusters based on the disparate threshold of similarity which produces the score of the cluster space that is favorable.

9. The method of claim 7, wherein generating one or more proximity matrices from the one or more feature vectors, further comprises:

generating, by the computer processor, the one or more proximity matrices, based on a probability density function of the one or more features of the plurality of clusters that are combined.

10. The method of claim 7, further comprising:

generating, by the computer processor, a sequence of models associated with the location of the natural resource, based, at least in part, on the one or more proximity matrices.

11. The method of claim 10, wherein generating a sequence of models associated with location of the natural resource, further comprises:

training, by the computer processor, the sequence of models based on clustering and successive refinement of supervised learning, wherein the sequence of models is based, at least in part, on the one or more proximity matrices.

12. The method of claim 10, wherein an output of a first model of the sequence of models is used as input to a second model of the mixture of models.

13. The method of claim 12, further comprising:

determining, by the computer processor, a route through the sequence of models for each hypothesis of the plurality of hypotheses, based on dimensions of evidence, wherein the dimensions of evidence include groupings of the one or more features.

14. The method of claim 1, further comprising:

representing, by the computer processor, the plurality of probabilities respectively corresponding to the validity of each hypothesis of the plurality of hypotheses as a heat map, wherein a first element of the heat map corresponds to a first probability of a hypothesis of the plurality of hypotheses, and disparate from a second element of the heat map corresponding to a second probability of a hypothesis of the plurality of hypotheses.

15. A computer program product for locating a natural resource, the computer program product comprising:

a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer processor to cause the computer processor to perform a method comprising;

generating a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource;

generating a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources;

determining a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data;

combining one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and

determining a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

16. The computer program product of claim 15, further comprising:

generating one or more feature vectors from the one or more features of the plurality of clusters;

generating a plurality of cluster spaces, which include the one or more pairs of clusters of the plurality of clusters that are combined, wherein each cluster space is based on a disparate threshold of similarity;

determining a cluster space that is favorable, based on a score of a cluster space of the plurality of cluster spaces and the disparate threshold of similarity; and

generating one or more proximity matrices from the one or more features vectors, based on the cluster space that is favorable.

17. The computer program product of claim 16, further comprising:

generating an sequence of models associated with the location of the natural resource, based, at least in part, on the one or more proximity matrices.

18. The computer program product of claim 17, wherein generating a sequence of models associated with the location of the natural resource, further comprises:

training a sequence of models, based on clustering and successive refinement of supervised learning, wherein the sequence of models is based, at least in part, on the one or more proximity matrices.

19. A computer system for locating a natural resource, the computer system comprising:

one or more computer processors;

one or more computer readable storage media;

program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising;

program instructions to generate a topic-based dataset based on parsing content received from a plurality of information sources, wherein the plurality of information sources includes historical data and scientific data, both of which are associated with a location of a natural resource;

program instructions to generate a plurality of clusters respectively corresponding to like-topic data of the topic-based dataset from the plurality of information sources;

program instructions to determine a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on one or more features extracted from the plurality of clusters of the like-topic data;

program instructions to combine one or more pairs of clusters of the plurality of clusters based, at least in part, on a similarity heuristic applied to the one or more pairs of clusters; and

program instructions to determine a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

20. The computer system of claim 19, wherein the topic-based dataset is weighted based on respective beta distributions of the topic-based data.