Abstract: A method, apparatus and computer program product for creating and operating a conversational system are described. A user annotated Web API specification is received which includes a set of user annotations The user annotated Web API specification is analyzed to produce an action catalog. The action catalog includes a set of sentences, each sentence describing a respective Web API call. The user annotated Web API specification is also analyzed to produce a rule set, each rule in the rule set has a rule condition which corresponds to an input in the user annotated Web API specification. The action catalog and the rule set are provided to the conversational system. The conversational system uses a question answering (QA) system as a natural language understanding (NLU) component. When the conversational system receives a user input during a dialog between a user and the conversational system, the user input is interpreted by the question answering (QA) system to identify an action from an action catalog.

Abstract: A method, apparatus and computer program product for creating and operating a conversational system are described. A user annotated API specification is received which includes a set of user annotations. The user annotated API specification is analyzed to produce an action catalog. The user annotated API specification is also analyzed to produce a rule set, each rule in the rule set has a rule condition which corresponds to an input in the user annotated API specification. The action catalog and the rule set are provided to the conversational system. The conversational system uses a question answering (QA) system as a natural language understanding (NLU) component. When the conversational system receives a user input during a dialog between a user and the conversational system, the user input is interpreted by the question answering (QA) system to identify an action from an action catalog. The identified action is set as a goal for a goal-based reasoning engine.

Abstract: A method, apparatus and computer program product for creating and operating a conversational system are described. A user annotated Web API specification is received which includes a set of user annotations The user annotated Web API specification is analyzed to produce an action catalog. The action catalog includes a set of sentences, each sentence describing a respective Web API call. The user annotated Web API specification is also analyzed to produce a rule set, each rule in the rule set has a rule condition which corresponds to an input in the user annotated Web API specification. The action catalog and the rule set are provided to the conversational system. The conversational system uses a question answering (QA) system as a natural language understanding (NLU) component. When the conversational system receives a user input during a dialog between a user and the conversational system, the user input is interpreted by the question answering (QA) system to identify an action from an action catalog.

Abstract: A production rules engine comprises: a class loader for loading a production rule ontology into a rule engine; a class loader for loading production rules into the rule engine, wherein a production rule comprises a rule and an action defined in the production rule ontology; instance creation logic for creating instances of production rules; a reasoner for executing the production rules and for locating an instance of a production rule having an inconsistency between an action to change the ontology and the existing ontology and for executing the production rules after inconsistencies have been fixed; a constraint engine for locating a solution to an inconsistent ontology; and a fixer for updating the inconsistent ontology with the located solution.

Abstract: An aspect of the invention includes transforming a source model to a target model. A source model is received and a transformation specification that includes a set of rules is accessed. Each rule includes a pattern description and a production component. The pattern description includes a pattern in the source model and the production component includes an algorithm for generating an output in the target model from the pattern. For each expression in the source model, the expression is decomposed into sub-expressions, matching rules in the transformation specification for the expression and the sub-expressions are detected, one of the matching rules having the most specific pattern description is selected, and the production component of the selected rule is executed to generate the output in the target model. The target model is created from the generated output.

Abstract: A method for forming and using a composite production rule may include compiling, by a computer system, a decision table or a decision tree to generate a composite production rule. The method may also include generating the composite production rule and selecting, by the computer system, an algorithm for compiling the composite production rule. The method may additionally include compiling, by the computer system, the composite production rule into an executable program based on pattern matching of the selected algorithm. The method may further include executing, by the computer system, the composite production rule to provide an output based on the composite production rule.

Abstract: A method for forming and using a composite production rule may include compiling, by a computer system, a decision table or a decision tree to generate a composite production rule. The method may also include generating the composite production rule and selecting, by the computer system, an algorithm for compiling the composite production rule. The method may additionally include compiling, by the computer system, the composite production rule into an executable program based on pattern matching of the selected algorithm. The method may further include executing, by the computer system, the composite production rule to provide an output based on the composite production rule.

Abstract: A production rules engine comprises: a class loader for loading a production rule ontology into a rule engine; a class loader for loading production rules into the rule engine, wherein a production rule comprises a rule and an action defined in the production rule ontology; instance creation logic for creating instances of production rules; a reasoner for executing the production rules and for locating an instance of a production rule having an inconsistency between an action to change the ontology and the existing ontology and for executing the production rules after inconsistencies have been fixed; a constraint engine for locating a solution to an inconsistent ontology; and a fixer for updating the inconsistent ontology with the located solution.

Abstract: A method for forming and using a composite production rule may include compiling, by a computer system, a decision table or a decision tree to generate a composite production rule. The method may also include generating the composite production rule and selecting, by the computer system, an algorithm for compiling the composite production rule. The method may additionally include compiling, by the computer system, the composite production rule into an executable program based on pattern matching of the selected algorithm. The method may further include executing, by the computer system, the composite production rule to provide an output based on the composite production rule.

Abstract: A method for forming and using a composite production rule may include compiling, by a computer system, a decision table or a decision tree to generate a composite production rule. The method may also include generating the composite production rule and selecting, by the computer system, an algorithm for compiling the composite production rule. The method may additionally include compiling, by the computer system, the composite production rule into an executable program based on pattern matching of the selected algorithm. The method may further include executing, by the computer system, the composite production rule to provide an output based on the composite production rule.

Abstract: Systems and methods for matching objects against a set of rules are described. The present invention is a novel rule execution algorithm that generally operates with greater efficiency than known algorithms. The algorithm uses a test analyzer to determine the relationships that exist between pairs of tests within a ruleset. Each rule is then translated into loops and tests, and merged into a unified series of loops and tests using the output of the test analyzer. The algorithm then generates pattern matching code corresponding to the unified series of loops and tests for evaluation by a virtual machine, and auxiliary code that provides object manipulations and rule actions at the service of the pattern matching code. In its runtime, the algorithm loads objects into the pattern matching code through an access interface. The pattern matching code is then executed by the virtual machine against the loaded objects.

Abstract: Systems and methods for matching objects against a set of rules are described. The present invention is a novel rule execution algorithm that generally operates with greater efficiency than known algorithms. The algorithm uses a test analyzer to determine the relationships that exist between pairs of tests within a ruleset. Each rule is then translated into loops and tests, and merged into a unified series of loops and tests using the output of the test analyzer. The algorithm then generates pattern matching code corresponding to the unified series of loops and tests for evaluation by a virtual machine, and auxiliary code that provides object manipulations and rule actions at the service of the pattern matching code. In its runtime, the algorithm loads objects into the pattern matching code through an access interface. The pattern matching code is then executed by the virtual machine against the loaded objects.