Method and system for learning rules from a data base

A system configured for learning rules from a data base, the data base including entities and binary relations between pairs of the entities, wherein each of the entities is either a non-numerical constant or a numerical value, and each binary relation of the binary relations between a non-numerical constant and a numerical value is a numerical binary relation and each binary relation of the binary relations between two constants is a non-numerical binary relation, the system comprising:

• a processing unit configured to:
• derive aggregate values from the numerical binary relations of the binary relations of the data base and/or the non-numerical binary relations of the binary relations of the data base;
• derive non-numerical binary relations from the aggregate values;
• add each of the derived non-numerical binary relations to the data base as a binary relation between two of the entities of the data base;
• construct differentiable operators, wherein each of the differentiable operators refers to a non-numerical binary relation of the data base or a derived non-numerical binary relation of the data base;
• extract rules from the differentiable operators using an artificial neural network based on neural logic programming (LP) framework, wherein the rules extracted from the differentiable operators are horn rules;
• decode the rules into decoded rules;
• and
• predict one or more relations between two or more entities of the data base by applying the rules and/or the decoded rules to the two or more entities of the data base.

A non-transitory computer readable storage medium on which is stored a computer program for learning rules from a data base, the data base including entities and binary relations between pairs of the entities, wherein each of the entities is either a non-numerical constant or a numerical value, and each binary relation between a non-numerical constant and a numerical value is a numerical binary relation and each binary relation between two non-numerical constants is a non-numerical binary relation, the computer program, when executed by a computer, causing the computer to perform the following steps:

• deriving aggregate values from the numerical binary relations of the binary relations of the data base and/or the non-numerical binary relations of the binary relations of the data base;
• deriving non-numerical binary relations from the aggregate values;
• adding each of the derived non-numerical binary relations to the data base as a binary relation between two of the entities of the data base;
• constructing differentiable operators, wherein each of the differentiable operators refers to a non-numerical binary relation of the data base or a derived non-numerical relation of the data base;
• extracting rules from the differentiable operators using an artificial neural network based on neural logic programming (LP) framework, wherein the rules extracted from the differentiable operators are horn rules;
• decode the rules into decoded rules;
• and
• predict one or more relations between two or more entities of the data base by applying the rules and/or the decoded rules to the two or more entities of the data base.

A method, comprising:

• providing a system configured for learning rules from a data base, the data base including entities and binary relations between the entities, wherein each of the entities is either a non-numerical constant or a numerical value, and each binary relation between a non-numerical constant and a numerical value is a numerical binary relation and each binary relation between two non-numerical constants is a non-numerical binary relation, the system including:
• a processing unit configured to:
• derive aggregate values from the numerical binary relations of the binary relations of the data base and/or the non-numerical binary relations of the binary relations of the data base;
• derive non-numerical binary relations from the aggregate values;
• add the derived non-numerical binary relations to the data base as a binary relation between two of the entities of the data base;
• construct differentiable operators, wherein each of the differentiable operators refers to a non-numerical binary relation of the data base or a derived non-numerical binary relation of the data base;
• and
• extract rules from the differentiable operators using an artificial neural network based on neural logic programming (LP) framework, wherein the rules extracted from the differentiable operators are horn rules;
• decode the rules into decoded rules;
• and
• predict one or more relations between two or more entities of the data base by applying the rules and/or the decoded rules to the two or more entities of the data base;
• using the system to predict relations between entities of the data base and/or clean the data base and/or complete the data base.