IMPLEMENTING SOCIALLY ENABLED BUSINESS RISK MANAGEMENT

• US 20160071035A1
• Filed: 09/05/2014
• Published: 03/10/2016
• Est. Priority Date: 09/05/2014
• Status: Active Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Chee, Yi-Min, Franceshini, Michele M., Jagmohan, Ashish, Lastras-Montano, Luis A., Mukherjee, Debdoot, Ratakonda, Krishna C., Khabiri, Elham
• Application Number:
  US14/478,516
• Publication Number:
  US 20160071035A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities

Abstract

A method that comprises receiving a plurality of inputs including data a plurality of multiple business repositories, generating from the plurality of inputs a corpus graph as a statistical relational network, inferring a plurality of new relations between informational elements of the statistical relational network, and generating a plurality of summaries of the plurality of new relations. Further, each summary describes the informational elements of the statistical relational network associated with a corresponding risk-relation.

Claims

1. A method, comprising:

receiving, by a processor, a plurality of inputs including data a plurality of multiple business repositories;

generating, by the processor, from the plurality of inputs a corpus graph as a statistical relational network;

inferring, by the processor, a plurality of new relations between informational elements of the statistical relational network; and

generating, by the processor, a plurality of summaries of the plurality of new relations, each summary describing the informational elements of the statistical relational network associated with that corresponding risk-relation.

2. The method of claim 1, wherein the a plurality of inputs includes risk or project management documents, each document including at least one of structured, semi-structured, and unstructured data, the data detailing at least one of a goal, a risk, a technology, and an expert associated with that document.

3. The method of claim 1, wherein the generating of the corpus graph from the plurality of inputs includes at least one of:

extracting corpus-wide risk concepts from the data;

identifying textual risk segments from the data; and

performing recognition operations on the data to produce recognized technology, expert, and client data.

4. The method of claim 3, further comprising:

utilizing at least one of the corpus-wide risk concepts, the textual risk segments, and the recognized data to build the statistical relational network.

5. The method of claim 4, wherein the links of the statistical relational network are associated with weights representing a probability that a connection has been correctly inferred.

6. The method of claim 1, wherein the inferring of the plurality of new relations between the informational elements of the statistical relational network associates unconnected informational elements.

7. The method of claim 6, wherein the plurality of new relations include a relation between at least one of a pair of project-project, project-expert, and project-risk informational elements.

8. The method of claim 6, wherein the inferring of the plurality of new relations includes inferring a new relation between an element of a first type and all elements of a second type includes:

computing a relatedness score between the element of the first type and each of the elements of the second type;

ranking the elements of the second type in descending order by the relatedness score;

selecting one or more of the elements of the second type with the highest scores.

9. The method of claim 8, wherein the first and second types are the same.

10. The method of claim 8, wherein the computing of the relatedness score is responsive to computing a stationary probability distribution of a random walk originating at the a of the first type.

11. The method of claim 1, wherein the plurality of summaries includes a summary for an informational element computed by selecting textual segments from unstructured textual data describing the informational element.

12. The method of claim 11 wherein the selecting of the textual segments is responsive to an occurrence of mentions of other informational elements in the unstructured data.

13. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause:

receiving, by the processor, a plurality of inputs including data a plurality of multiple business repositories;

generating, by the processor, from the plurality of inputs a corpus graph as a statistical relational network;

inferring, by the processor, a plurality of new relations between informational elements of the statistical relational network; and

generating, by the processor, a plurality of summaries of the plurality of new relations, each summary describing the informational elements of the statistical relational network associated with that corresponding risk-relation.

14. The computer program product of claim 13, wherein the a plurality of inputs includes risk or project management documents, each document including at least one of structured, semi-structured, and unstructured data, the data detailing at least one of a goal, a risk, a technology, and an expert associated with that document.

15. The computer program product of claim 13, wherein the generating of the corpus graph from the plurality of inputs includes at least one of:

extracting corpus-wide risk concepts from the data;

identifying textual risk segments from the data; and

performing recognition operations on the data to produce recognized technology, expert, and client data.

16. The computer program product of claim 15, wherein the program instructions are further executable by the processor to cause:

utilizing at least one of the corpus-wide risk concepts, the textual risk segments, and the recognized data to build the statistical relational network.

17. A system, comprising a processor and a memory, the system configured to:

receive a plurality of inputs that include data a plurality of multiple business repositories;

generate from the plurality of inputs a corpus graph as a statistical relational network;

infer a plurality of new relations between informational elements of the statistical relational network; and

generate a plurality of summaries of the plurality of new relations, where each summary describes the informational elements of the statistical relational network associated with that corresponding risk-relation.

18. The system of claim 17, wherein the a plurality of inputs includes risk or project management documents,

wherein each document includes at least one of structured, semi-structured, and unstructured data, the data details at least one of a goal, a risk, a technology, and an expert associated with that document.

19. The system of claim 17, wherein the generation of the corpus graph from the plurality of inputs includes at least one of:

an extraction corpus-wide risk concepts from the data;

identification textual risk segments from the data; and

performance recognition operations on the data to produce recognized technology, expert, and client data.

20. The system of claim 19, wherein the system is further configured to:

utilize at least one of the corpus-wide risk concepts, the textual risk segments, and the recognized data to build the statistical relational network.