Systems, methods, and computer program products for generating a query specific Bayesian network

A decision support system, comprising:

• a memory configured to provide an unconstrained domain model defined by domain concepts and causal relationships between the domain concepts, wherein each causal relationship includes a value for the weight of causal belief for the causal relationship;
• and
• processing circuitry configured to transform, in response to a query, the unconstrained domain model into a query specific Bayesian network for the domain model by identifying one or more cycles in the unconstrained domain model;
• eliminating the one or more cycles from the unconstrained domain model;
• identifying a sub-graph of the unconstrained domain model that is relevant to the query and creating one or more conditional probability tables that comprise the query specific Bayesian network,
• wherein eliminating the one or more cycles comprises identifying the causal relationships between the domain concepts that are weak relative to other causal relationships in the unconstrained domain model as a result of having a weight with an absolute value that fails to satisfy a predefined threshold that is based upon a representation of the weights of a plurality of the causal relationships in the unconstrained domain model, and removing the causal relationships between the domain concepts that are identified to be weak,
• wherein the processing circuitry is configured to utilize the query specific Bayesian network to analyze textual data by performing textual and reasoning processing and to provide output based upon probabilistic predictions based thereupon.

A computer program product of a decision support system comprising a non-transitory computer-useable medium having control logic stored therein, the control logic comprising:

• a first code configured to providing an unconstrained domain model defined by domain concepts and causal relationships between the domain concepts, wherein each causal relationship includes a value for the weight of causal belief for the causal relationship;
• and
• a second code configured to transform, in response to a query, the unconstrained domain model into a query specific Bayesian network for the domain model by identifying one or more cycles in the unconstrained domain model;
• eliminating the one or more cycles from the unconstrained domain model;
• identifying a sub-graph of the unconstrained domain model that is relevant to the query and creating one or more conditional probability tables that comprise the query specific Bayesian network,
• wherein eliminating the one or more cycles comprises identifying the causal relationships between the domain concepts that are weak relative to other causal relationships in the unconstrained domain model as a result of having a weight with an absolute value that fails to satisfy a predefined threshold that is based upon a representation of the weights of a plurality of the causal relationships in the unconstrained domain model, and removing the causal relationships between the domain concepts that are identified to be weak,
• wherein the query specific Bayesian network is configured to analyze textual data by performing textual and reasoning processing and to provide output based upon probabilistic predictions based thereupon.