Ontology-driven information system

• US 7,567,915 B2
• Filed: 10/20/2006
• Issued: 07/28/2009
• Est. Priority Date: 08/20/1999
• Status: Expired due to Fees

• Current Assignee: DST Systems Incorporated (SS&C Technologies Holdings Incorporated)
• Original Assignee: 6100198 Canada Incorporated
• Inventors: Hammitt, Lisa C., Beckert, Jorg
• Primary Examiner(s): Jeanty; Romain
• Application Number:
  US11/583,945
• Publication Number:
  US 20070038500A1
• Time in Patent Office: 1,012 Days
• Field of Search: 705/8, 705/7, 705/27
• US Class Current: 705/7.11
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0641 Shopping interfaces

Abstract

An ontology-driven information system includes a plurality of models, each of which expresses an aspect of a business domain using concepts and relationships between concepts. An ontology, which is in communication with each of the plurality of models, provides uniform definitions for the concepts and relationships between concepts used in the plurality of models. A method for executing an interaction flow model includes receiving an event and categorizing the received event. Once the event is categorized, a situation that matches the categorized received event is identified. One or more tasks are then executed for the situation. The execution of the one or more tasks can include either an interpretation of a model or the execution of a method of an object. The information system also includes a user and application interface and a reasoning engine that is in communication with the user and application interface. A knowledge manager is in communication with the user and application interface and is interfaced with the reasoning engine. A distributed information service also is in communication with the reasoning engine, the knowledge manager, and the user and application interface.

Claims

1. An information system executed on one or more particular machines, comprising:

a user and application interface;

a reasoning engine in communication with the user and application interface;

a knowledge manager in communication with the user and application interface and interfaced with the reasoning engine; and

a distributed information service in communication with the reasoning engine, the knowledge manager, and the user and application interface;

wherein the reasoning engine is configured to work in conjunction with the knowledge manager so as to enable the reasoning engine to handle events by executing one or more specific tasks prescribed by the knowledge manager to handle the events most appropriately; and

wherein the reasoning engine includes;

an interaction flow engine;

an inference engine;

a constraint satisfaction engine;

an optimization engine;

a categorization engine; and

a data mining engine;

wherein the interaction flow engine is in communication with the categorization engine, the inference engine, the constraint satisfaction engine and the optimization engine, the interaction flow engine being configured to invoke one or more of the categorization engine, the inference engine, the constraint satisfaction engine and the optimization engine to generate recommendations based on a profile of a user, rules obtained from the knowledge manager, constraints obtained from the knowledge manager, and an ontology.

2. An information system as recited in claim 1, wherein the knowledge manager includes an interaction flow model that is a repository for abstract situations to handle the events received by the reasoning engine, the situations defining the one or more tasks that are to be executed by the reasoning engine.

3. An information system as recited in claim 2, wherein the reasoning engine includes an interaction flow engine that is configured to receive the events, to invoke a categorization engine to categorize the events, and to interface with the interaction flow model of the knowledge manager.

4. An information system as recited in claim 3, wherein the interaction flow engine is configured to process through the one or more tasks by invoking at least one of an inference engine, a constraint satisfaction engine, an optimization engine, and an external application.

5. An information system as recited in claim 4, wherein the inference engine, which is part of the reasoning engine, executes at least one of the events in conjunction with rules obtained from a rule base model of the knowledge manager to generate a number of constraints that are communicated to the interaction flow engine of the reasoning engine.

6. An information system as recited in claim 5, wherein the constraint satisfaction engine executes at least one of the constraints received from the interaction flow engine and at least one of the constraints obtained from the constraint model, the constraint satisfaction engine being configured to produce a set of solutions.

7. An information system as recited in claim 6, wherein the optimization engine is configured to receive the set of solutions from the interaction flow engine and optimization model data to generate an optimized solution, the optimized solution being communicated to the distributed information service.

8. An information system as recited in claim 7, wherein the optimized solution is recorded.

9. An information system as recited in claim 1, wherein the profile includes interests of the user and wherein the constraints include business expertise and goals of the user.

10. An information system as recited in claim 1, wherein the interaction flow engine is configured to specify reasons regarding the generated recommendations, such reasons including identifying feedback received from the user, optimization models used to generate the recommendations, and the rules used to generate the recommendations.

11. An information system as recited in claim 1, wherein the interaction flow engine implements an interaction flow model of the knowledge manager to drive the generated recommendations.

12. An information system as recited in claim 1, wherein the knowledge manager includes:

an interaction flow model;

a rule base model;

a constraint model;

an optimization model;

a conceptual model;

a predictive model; and

the ontology.

13. An information system as recited in claim 12, wherein each of the interactive flow model and the ontology is in communication with the rule base model, the constraint model, and the optimization model, wherein the interactive flow model is configured to manage interaction flows with each of the rule base model, the constraint model, and the optimization model, and wherein interaction flows include a number of situations and each situation has a context description that contains event concepts that a situation of the number of situations requires to occur.

14. An information system as recited in claim 13, wherein the interaction flow model is configured to be compiled for execution by the interaction flow engine.

15. An information system as recited in claim 14, wherein the ontology of the knowledge manager defines a meaning of terms used by the interaction flow model.