Detecting and correcting knowledge base errors

A computer program product comprising:

• one or more computer readable storage media;
• and
• program instructions stored on the one or more computer readable storage media to perform operations comprising:
• receiving input data corresponding to a knowledge base, the input data including a plurality of propositional logic clauses;
• generating output data corresponding to a first set of logical rules and a first set of facts based on the plurality of propositional logic clauses;
• accumulating the first set of facts to generate accumulated facts;
• generating a logical neural network (LNN) graph based on the accumulated facts and the first set of logical rules, wherein the LNN graph comprises neurons corresponding to the plurality of propositional logic clauses and provides one-to-one correspondence between the neurons and logical elements represented in the plurality of propositional logic clauses;
• alternating reasoning and learning passes at the LNN graph until convergence to generate a second set of logical rules and a second set of facts;
• generating a third set of logical rules and a third set of facts, wherein the third set of logical rules and the third set of facts exceed a first predetermined threshold;
• and
• correcting errors in the knowledge base by updating the knowledge base to include the third set of logical rules and the third set of facts.

A computer system comprising:

• a processor set;
• one or more computer readable storage media;
• and
• program instructions stored on the one or more computer readable storage media to cause the processor set to perform operations comprising:
• receiving input data corresponding to a knowledge base, the input data including a plurality of propositional logic clauses;
• generating output data corresponding to a first set of logical rules and a first set of facts based on the plurality of propositional logic clauses;
• accumulating the first set of facts to generate accumulated facts;
• generating a logical neural network (LNN) graph based on the accumulated facts and the first set of logical rules, wherein the LNN graph comprises neurons corresponding to the plurality of propositional logic clauses and provides one-to-one correspondence between the neurons and logical elements represented in the plurality of propositional logic clauses;
• alternating reasoning and learning passes at the LNN graph until convergence to generate a second set of logical rules and a second set of facts;
• generating a third set of logical rules and a third set of facts, wherein the third set of logical rules and the third set of facts exceed a first predetermined threshold;
• and
• correcting errors in the knowledge base by updating the knowledge base to include the third set of logical rules and the third set of facts.