COGNITIVE MATCHING OF NARRATIVE DATA

• US 20160132591A1
• Filed: 11/10/2014
• Published: 05/12/2016
• Est. Priority Date: 11/10/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Clark, Adam T., Huebert, Jeffrey K., Payton, Aspen L., Petri, John E.
• Granted Patent: US 10,572,589 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A computer-implemented method for cognitive matching of narrative data may include collecting a set of data for a party and determining, by analyzing the set of data, an identifiable event for the party. In addition, the method may include identifying, using the identifiable event, a relevant feature of a corpus and providing an output corresponding to the relevant feature.

Claims

1. A computer implemented method for cognitive matching of narrative data, the method comprising:

collecting a set of data for a party;

determining, by analyzing the set of data, an identifiable event for the party;

identifying, using the identifiable event, a relevant feature of a corpus; and

providing an output corresponding to the relevant feature.

2. The method of claim 1, wherein collecting the set of data for the user includes aggregating user generated data selected from a group consisting of:

a web page, a user profile, a user status, a comment, or a blog.

3. The method of claim 1, wherein the corpus includes a narrative content database having one or more types of narrative content selected from a group consisting of:

literature data, film data, television data, audio data, and electronic game data.

4. The method of claim 3, wherein the narrative content is structured in a set of ontological relationships.

5. The method of claim 1, wherein determining, by analyzing the set of data, the identifiable event for the party includes:

extracting metadata from the set of data;

determining, based on the metadata of the set of data, a prominence factor of the set of data; and

ascertaining that the prominence factor of the set of data achieves a prominence criterion.

6. The method of claim 5, further comprising:

parsing, by a natural language processing technique configured to analyze semantic and syntactic content, unstructured data of the set of data;

identifying, from the unstructured data of the set of data, a set of story elements;

extracting, using the natural language processing technique, a sentiment feature coupled with the set of story elements; and

establishing, using the set of story elements and the sentiment feature, a framework component.

7. The method of claim 1, identifying, using the identifiable event, the relevant feature of the corpus includes:

comparing a framework component to narrative content of a narrative content database; and

determining, based on a set of story elements and a sentiment feature of the framework component and a set of ontological relationships of the narrative content, the relevant feature corresponding to the identifiable event.

8. The method of claim 1, wherein providing the output corresponding to the relevant feature includes:

generating, based on the relevant feature, a set of content suggestions; and

transmitting, to a user, the set of content suggestions.

9. The method of claim 8, further comprising assigning, to the set of content suggestions, a confidence score based on a relationship between the framework component and the relevant feature.

10. The method of claim 1, further comprising:

receiving a set of user ratings for the output; and

defining, using a machine learning technique configured to process the set of user ratings for the output, a decision parameter for identifying the relevant feature of the corpus.

11-20. -20. (canceled)