Knowledge discovery agent system
A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.
1. A personal search agent system, comprising:
a user agent configured to:
transform a query from a user into a search task assignment,
send the search task assignment to one or more server-based search manager agents, and
receive one or more responses to the search task assignment;
a user miner agent configured to:
observe the user's interaction with the user agent or agents, and
learn the behavior and preferences of the user; and
a data mining agent configured to:
analyze the responses to the user's queries, and
build a conceptual network of the knowledge contained in the response,
wherein the data mining agent further learns associations of natural language artifacts in unstructured data sources, wherein said artifacts include at least one of words, phrases, subjects, predicates, modifiers, and other syntactic forms,
wherein learned associations resulting from said learning associations of natural language artifacts are formed using grouping of one natural language artifact with another at least one natural language artifact, based on a criteria of shared features of one or more sets from said grouping constituting measurements from said data sources,
wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations, and
wherein said grouping creates a network of conditional probabilities between all encountered natural language artifacts or a subset thereof, determined by consideration of conditional interaction probabilities based on a history of measurements from said data sources.
2. The system of claim 1 , further wherein learned associations are represented in a specific format compatible for operations for mapping between a plurality of data structures and languages comprising one or more of arrays, vector spaces, first order predicate logic, Conceptual Graphs, SQL, and typed programming languages.
3. The system of claim 2 , further wherein said typed program languages comprise one or more of Java, C++, and other conventional programming languages.
4. The system of claim 1 , further wherein hierarchies of association are constructed across a state space of term usage compatible for interpolation of mapping functions between sets of terms.
5. The system of claim 4 , further wherein said mapping functions include one or more of fuzzy-type, weighted-type, or other types of mapping functions.
6. The system of claim 1 , further wherein a structure of mapping functions is generated, said structure comprising a formal semantic structure of one or more of programming languages, modal logics, frame systems, and ontologies of objects and relationships.
7. A personal search agent system, comprising:
a user agent configured to:
transform a query from a user into a search task assignment,
send the search task assignment to one or more server-based search manager agents, and
receive one or more responses to the search task assignment;
a user miner agent configured to:
observe the user's interaction with the user agent or agents, and
learn the behavior and preferences of the user; and
a data mining agent configured to:
analyze the responses to the user's queries,
build a conceptual network of the knowledge contained in the response, and
learn associations of natural language artifacts in unstructured data sources,
wherein said artifacts include at least one of words, phrases, subjects, predicates, modifiers, and other syntactic forms, and
wherein hierarchies of association are constructed across a state space of term usage compatible for interpolation of mapping functions between sets of terms.
8. The system of claim 7 , further wherein said mapping functions include one or more of fuzzy-type, weighted-type, or other types of mapping functions.
9. The system of claim 7 , further wherein learned associations resulting from said learning associations of natural language artifacts are formed using grouping of one natural language artifact with another at least one natural language artifact, based on a criteria of shared features of one or more sets from said grouping constituting measurements from said data sources.
10. The system of claim 9 , further wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations.
11. The system of claim 10 , further wherein said grouping creates a network of conditional probabilities between all encountered natural language artifacts or a subset thereof, determined by consideration of conditional interaction probabilities based on a history of measurements from said data sources.
12. The system of claim 7 , further wherein learned associations are represented in a specific format compatible for operations for mapping between a plurality of data structures and languages comprising one or more of arrays, vector spaces, first order predicate logic, Conceptual Graphs, SQL, and typed programming languages.
13. The system of claim 12 , further wherein said typed program languages comprise one or more of Java, C++, and other conventional programming languages.
14. The system of claim 7 , further wherein a structure of mapping functions is generated, said structure comprising a formal semantic structure of one or more of programming languages, modal logics, frame systems, and ontologies of objects and relationships.