Abstract: Techniques for facilitating learning of one or more ontological rules of a resource description framework database are provided. The techniques include obtaining ontology vocabulary from a resource description framework database, generating a rule hypothesis by incrementally building upon a previously learnt rule from the database by adding one or more predicates to the previously learnt rule, performing a constraint check on the generated rule hypothesis by determining compatibility with each previously learnt rule to ensure that a complete rule set including each previously learnt rule and the generated rule hypothesis is consistent, validating the rule hypothesis as a rule using one or more association rule mining techniques to determine validity of the rule hypothesis against the database, and applying the rule to the database to infer one or more facts from the database to facilitate learning of one or more additional ontological rules.

Abstract: A system and computer program product for facilitating learning of one or more ontological rules of a resource description framework database include obtaining ontology vocabulary from a resource description framework database, generating a rule hypothesis by incrementally building upon a previously learnt rule from the database by adding one or more predicates to the previously learnt rule, performing a constraint check on the generated rule hypothesis by determining compatibility with each previously learnt rule to ensure that a complete rule set including each previously learnt rule and the generated rule hypothesis is consistent, validating the rule hypothesis as a rule using one or more association rule mining techniques to determine validity of the rule hypothesis against the database, and applying the rule to the database to infer one or more facts from the database to facilitate learning of one or more additional ontological rules.

Abstract: A self-organizing computing machine utilizes a method for mapping from a plurality of patterns contained within provided inputs to an invariant perception, distinguishable by a name or a label. The self-organizing computing machine includes a network of at least three nodes arranged in at least two hierarchical levels, at least one feature extractor, and at least one output unit arranged to interface the invariant perception. The nodes may include a reinforcement learning sub-network combined with an ensemble learning sub-network. The reinforcement learning sub-network may be arranged to receive at least two correlants, to determine a plurality of output values and to output the output values to the nodes of the higher level and the nodes of the lower level. Also, the ensemble learning sub-network may be arranged to receive and to combine output values from nodes of the higher level and nodes of the lower level.