System, method, and computer program product for combination of cognitive causal models with reasoning and text processing for knowledge driven decision support
First Claim
1. A system for assisting knowledge driven decision support, comprising:
- a processor configured to operate a directed acyclic graph Bayesian network of an unconstrained causal domain model, a text processing tool, and a reasoning tool;
the unconstrained causal domain model configured to define and store, on a computer-readable medium, at least two domain concepts and at least one causal relationship between the domain concepts, wherein the directed acyclic graph Bayesian network is configured to represent a formalization of the unconstrained causal domain model with minimal information loss by eliminating cycles of the unconstrained causal domain model by computing information gain and eliminating influence arcs of the unconstrained causal domain model that minimizes information loss;
the text processing tool configured to analyze at least one document using the directed acyclic graph Bayesian network to produce at least one text profile for the at least one document; and
the reasoning tool configured to determine a result by analyzing the at least one text profile using at least one domain concept of the directed acyclic graph Bayesian network and one causal relationship of the domain concept, wherein the reasoning tool is further configured to use the text processing tool to use the directed acyclic graph Bayesian network, and wherein the reasoning tool is further configured to electronically present a display identifying the result of the analysis of the reasoning tool, providing a user the ability to use the result in a decision making process.
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Abstract
A system, method, and computer program product for combining causal domain models with reasoning and text processing for knowledge driven decision support are provided. A knowledge driven decision support system is capable of creating a domain model, extracting and processing quantities of text according to the domain model, and generating understanding of the content and implications of information sensitive to analysts. An interface may be used to receive input to model complex relationships of a domain, establish implications of interest or request a query, and update the causal model. A processing element can capture and process text into text profiles by incorporating the domain model and process the text profiles in accordance with the domain model by applying formal reasoning to the information to derive trends, predict events, or arrive at other query results. An output element can provide a user the resulting information related to the domain model.
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Citations
29 Claims
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1. A system for assisting knowledge driven decision support, comprising:
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a processor configured to operate a directed acyclic graph Bayesian network of an unconstrained causal domain model, a text processing tool, and a reasoning tool; the unconstrained causal domain model configured to define and store, on a computer-readable medium, at least two domain concepts and at least one causal relationship between the domain concepts, wherein the directed acyclic graph Bayesian network is configured to represent a formalization of the unconstrained causal domain model with minimal information loss by eliminating cycles of the unconstrained causal domain model by computing information gain and eliminating influence arcs of the unconstrained causal domain model that minimizes information loss; the text processing tool configured to analyze at least one document using the directed acyclic graph Bayesian network to produce at least one text profile for the at least one document; and the reasoning tool configured to determine a result by analyzing the at least one text profile using at least one domain concept of the directed acyclic graph Bayesian network and one causal relationship of the domain concept, wherein the reasoning tool is further configured to use the text processing tool to use the directed acyclic graph Bayesian network, and wherein the reasoning tool is further configured to electronically present a display identifying the result of the analysis of the reasoning tool, providing a user the ability to use the result in a decision making process. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method, comprising:
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providing an unconstrained causal domain model defined by domain concepts and causal relationships between the domain concepts; transforming, using a processor, the unconstrained causal domain model into a directed acyclic graph Bayesian network formalism with minimal information loss, wherein the transformation comprises eliminating cycles of the unconstrained causal domain model by computing information gain and eliminating influence arcs of the unconstrained causal domain model that minimizes information loss; storing on a computer-readable medium, the directed acyclic graph Bayesian network formalism; analyzing text using the direct acyclic graph Bayesian network formalism to extract information and derive test profiles; storing, on a computer-readable medium, the text profiles; performing reasoning analysis of the text profiles according to the directed acyclic graph Bayesian network formalism to derive a result; and electronically presenting a display identifying the result of the reasoning analysis, providing a user the ability to use the result in a decision making process. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. A computer-readable medium encoded with a computer program for causing a processor to support a decision using an unconstrained causal domain model, the computer program comprising:
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a first code for causing the processor to provide the unconstrained causal domain model; a second code for causing the processor to transform the unconstrained causal domain model into a directed acyclic graph Bayesian network formalism with minimal information loss by eliminating cycles of the unconstrained causal domain model by computing information gain and eliminating influence arcs of the unconstrained causal domain model that minimizes information loss and store, on a computer-readable medium, the directed acyclic graph Bayesian network formalism; and a third code for causing the processor to analyze text using the directed acyclic graph Bayesian network formalism to derive text profiles and store, on a computer-readable medium, the text profiles; a fourth code for causing the processor to perform reasoning analysis of the text profiles according to the directed acyclic graph Bayesian network formalism. - View Dependent Claims (23, 24, 25, 26, 27, 28, 29)
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Specification