Systems and methods for semantic concept definition and semantic concept relationship synthesis utilizing existing domain definitions
A method for assessing the coherence of an input with a data processing system using synthesized concepts is provided. The method includes obtaining an active concept definition from the input of a cognitive agent, extracting real concept definitions composed of a set of attributes from an analyzed domain, matching the active concept definition to the extracted definitions, deriving virtual concept definitions from the real concept definitions using a semantic processing protocol such that the derived virtual concept definitions form a tree-structure graph of concepts and concept relationships, and measuring the attribute set coherence of the virtual concept definitions using a confidence gradient. The confidence gradient is based on at least one metric of relative proximity and co-occurrence. The method further includes assessing the probability of coherence, of the input with the data processing system, based on the measure of coherence within the confidence gradient.
1 . A method for assessing the coherence of an input with a data processing system using synthesized concepts, the method comprising:
obtaining an active concept definition from the input of a cognitive agent;
extracting a plurality of real concept definitions composed of a set of attributes from an analyzed domain;
matching the active concept definition to the extracted real concept definitions;
deriving a plurality of virtual concept definitions from the real concept definitions using a semantic processing protocol, such that the derived virtual concept definitions form a tree-structure graph of concepts and concept relationships;
measuring an attribute set coherence of the virtual concept definitions using a confidence gradient, wherein the confidence gradient is based on at least one metric of relative proximity and co-occurrence; and
assessing the probability of coherence, of the input with the data processing system, based on the measure of coherence within the confidence gradient.
2 . The method of claim 1 , wherein the cognitive agent is one of a human, multiple humans, an expert system, a neural network, and an evolutionary system.
3 . The method of claim 1 , further comprising synthesizing additional possible virtual concept definitions using the virtual concept definitions.
4 . The method of claim 1 , wherein the synthesized concepts and concept definitions are generated by the data processing system.
5 . The method of claim 1 , wherein a depth of a hierarchy of virtual concept definitions is selectable.
6 . The method of claim 5 , wherein the selection of the depth of the hierarchy is based upon the confidence gradient.
7 . The method of claim 1 , further comprising searching a plurality of domains to build a selectable quantity of virtual concept definitions.
8 . The method of claim 1 , wherein the virtual concept definitions are in a poly-hierarchal relationship with the real concept definitions.
9 . The method of claim 1 , wherein a scope of the virtual concept definitions is variable with respect to a change in a relative proximity measure between attributes in the set of attributes.
10 . The method of claim 1 , wherein the semantic processing protocol is based upon one of formal concept analysis, faceted classification synthesis, and concept inferencing.
11 . A system for assessing the coherence of an input with a data processing system using synthesized concepts, the system comprising:
at least one processor, operable to execute executable instructions stored in at least one tangible memory, to:
obtain an active concept definition from the input of a cognitive agent;
extract a plurality of real concept definitions composed of a set of attributes from an analyzed domain;
match the active concept definition to the extracted real concept definitions;
derive a plurality of virtual concept definitions from the real concept definitions using a semantic processing protocol, such that the derived virtual concept definitions form a tree-structure graph of concepts and concept relationships;
measure an attribute set coherence of the virtual concept definitions using a confidence gradient, wherein the confidence gradient is based on at least one metric of relative proximity and co-occurrence; and
assess the probability of coherence of the input with the data processing system based on the measure of coherence within the confidence gradient.
12 . The system of claim 11 , wherein the cognitive agent is one of a human, multiple humans, an expert system, a neural network, and an evolutionary system.
13 . The system of claim 11 , wherein the at least one processor further executes the instructions to synthesize additional possible virtual concept definitions using the virtual concept definitions.
14 . The system of claim 11 , wherein the synthesized concepts and concept definitions are generated by the data processing system.
15 . The system of claim 11 , wherein a depth of a hierarchy of virtual concept definitions is selectable.
16 . The system of claim 15 , wherein the selection of the depth of the hierarchy is based upon the confidence gradient.
17 . The system of claim 11 , wherein the at least one processor further executes the instructions to search a plurality of domains to build a selectable quantity of virtual concept definitions.
18 . The system of claim 11 , wherein the virtual concept definitions are in a poly-hierarchal relationship with the real concept definitions.
19 . The system of claim 11 , wherein a scope of the virtual concept definitions is variable with respect to a change in a relative proximity measure between attributes in the set of attributes.
20 . The system of claim 11 , wherein the semantic processing protocol is based upon one of formal concept analysis, faceted classification synthesis, and concept inferencing.