Methods and systems for context-aware entity correspondence and merging

• US 8,903,848 B1
• Filed: 04/07/2011
• Issued: 12/02/2014
• Est. Priority Date: 04/07/2011
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Bahrami, Ali, Yuan, Jun
• Primary Examiner(s): Yen, Syling
• Application Number:
  US13/082,121
• Time in Patent Office: 1,335 Days
• Field of Search: 707/706, 707/713, 707/722, 707/736, 707/739, 707/758, 707/759, 707/769, 707/781, 707/804, 707999002- 6, 455/456.2
• US Class Current: 707/769
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 16/285 Clustering or classification

Abstract

A computer-based method for correlating relevant information from multiple entities based on contextual correspondence is described. The method includes receiving, at a computer, information relating to a plurality of the multiple entities, the information including data features and context features, correlating the data features utilizing one or more algorithms running on the computer, correlating the context features utilizing one or more algorithms running on the computer, and aggregating the plurality of the multiple entities based on both a correspondence of the data features and a correspondence of the context features for at least one of storage in a memory associated with the computer and output as data from the computer.

Claims

1. A computer-based method for correlating relevant information from multiple entities based on contextual correspondence, said method comprising:

receiving, at a computer, information relating to a plurality of the multiple entities, the information including data features and context features;

correlating the data features utilizing one or more algorithms running on the computer;

correlating the context features utilizing the one or more algorithms running on the computer;

aggregating the plurality of the multiple entities based on a correspondence of the data features, a correspondence of the context features, and at least one context model for at least one of storage in a memory associated with the computer and output as data from the computer, wherein the at least one context model includes temporal information associated with the context features;

receiving a first contextual event including temporal information at a first location associated with the first contextual event;

receiving a second contextual event including temporal information at a second location associated with the second contextual event, wherein the first contextual event is different from the second contextual event and the first location is different from the second location;

matching a plurality of entities based on the received contextual events;

determining a correspondence value for the matched plurality of entities based on a comparison of at least one of the data features or context features in light of the temporal information of the first contextual event and the first location;

dynamically updating the correspondence value for the matched plurality of entities based on a comparison of at least one of the data features or context features in light of the temporal information of the second contextual event and the second location; and

outputting the matched plurality of entities that exceed a predetermined correspondence value.

2. The method according to claim 1 further comprising storing the context features associated with an entity in a repository.

3. The method according to claim 1 wherein correlating the context features comprises correlating context features that are dynamic over time.

4. The method according to claim 1 wherein aggregating the plurality of the multiple entities comprises filtering the correlated context features to provide relevant information to an information consumer.

5. The method according to claim 1 wherein correlating the context features comprises correlating the context features based on a specific purpose defined by an information consumer.

6. The method according to claim 5 further comprising using changing contextual correlations to predict future information needs for the information consumer.

7. The method according to claim 1 wherein correlating the context features comprises correlating the context features to identify and manage relationship among the plurality of the multiple entities.

8. The method according to claim 1 wherein correlating the context features comprises leveraging existing contextual ontologies with mappings and conversions represented by both ontological predicates and pluggable reasoning services.

9. The method according to claim 1 wherein aggregating the plurality of the multiple entities comprises calculating information relationships and composing information objects that merge the received information.

10. The method according to claim 1 wherein aggregating the plurality of the multiple entities comprises binding temporal and contextual information relevancy and any information regarding anticipated future context-based entity needs.

11. The method according to claim 1 wherein correlating the context features comprises using at least one of deterministic, probabilistic, and fuzzy logic matching algorithms to correlate the context features.

12. The method according to claim 1 wherein correlating the context features further comprises:

utilizing ontologies and algorithms to enrich the semantics of contextual content by tagging data models and data instances with context ontologies; and

providing varying degrees of domain-customized generality and specialization to address dynamically changing information space.

13. The method according to claim 1 wherein correlating the context features further comprises:

using semantic rules to compose and decompose existing alignments within a hierarchical structure; and

using semantic rules to append and relax at least one of alignment conditions and confidence levels for approximations, such that the adaptability and reliability of context data management and dissemination in dynamic environments is increased.

14. One or more computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to:

receive information relating to a plurality of the entities, the information including data features and context features;

separately correlate the data features and the context features;

aggregate the plurality of entities based on a correspondence of the data features, a correspondence of the context features, and at least one context model that includes temporal information associated with the context features;

receive a first contextual event including temporal information, wherein the first contextual event is associated with a first location;

receive a second contextual event including temporal information, wherein the second contextual event is different from the first contextual event, and the second contextual event is associated with a second location different from the first location;

match a plurality of entities based on the received contextual events;

determine a correspondence value for the matched plurality of entities based on a comparison of at least one of the data features or context features in light of the temporal information of the first contextual event and the first location;

dynamically update a correspondence value for the matched plurality of entities based on a comparison of at least one of the data features or context features in light of the temporal information of the second contextual event and the second location; and

output the matched plurality of entities that exceed a predetermined correspondence value.

15. One or more computer-readable storage media according to claim 14 wherein the computer-executable instructions cause the at least one processor to leverage existing contextual ontologies with mappings and conversions represented by both ontological predicates and pluggable reasoning services to correlate the contextual features.

16. One or more computer-readable storage media according to claim 14 wherein the computer-executable instructions cause the at least one processor to use at least one of deterministic, probabilistic, and fuzzy logic matching algorithms to correlate the context features.

17. One or more computer-readable storage media according to claim 14 wherein the computer-executable instructions cause the at least one processor to correlate the context features such that relationships among the plurality of entities are identified and managed.