Abstract: According to one embodiment, categorizing concept types of a conceptual graph includes receiving the conceptual graph comprising one or more concept types, one or more relationship types, and one or more arcs. Each concept type is categorized according to the relationship types and the arcs. The categorization of the each concept type is recorded.

Abstract: In one embodiment, searching documents includes generating a query conceptual graph for a search query. The query conceptual graph has graph terms representing concept types. A set of conceptually similar terms is identified for each graph term, and the set is mapped to the concept type represented by the graph term. Documents are searched in accordance with the set of conceptually similar terms. In another embodiment, selecting a document includes determining documents that satisfy a search query expressed as a query conceptual graph. A document conceptual graph that has graph terms representing concept types is generated for each document. A set of conceptually similar terms is identified for each graph term, where the set is mapped to the concept type represented by the graph term. A document is selected if terms of the sets associated with the document match terms associated with the query conceptual graph.

Abstract: According to one embodiment, attributes associated with a concept type of a query conceptual graph are received. A potentially conceptually similar term is received from an onomasticon. The potentially conceptually similar term is validated according to the attributes. According to another embodiment, attributes associated with a concept type of a document conceptual graph are received. A potentially conceptually similar term is received from an onomasticon. The potentially conceptually similar term is validated according to the attributes.

Abstract: Disclosed is a system and method for expanding variables within a computational model. The computational model, which can be a Bayesian-network, includes input and output variables that are interrelated via a conditional probability table. Term expansion is accomplished via a lexical database and a logic engine to determine semantic equivalents that are relevant to the computational model. The expanded terms allow the computational model to be related to instance data, which may be in the form of a dynamic ontology. Input variable expansion permits the computational model to be populated with semantically relevant instance data from the ontology, and output variable expansion permits the computational model to be associated with semantically relevant ontology nodes.

Abstract: Disclosed is a system and method for determining the probability of an event occurring. The method involves developing models relating a number of factors and variables. The factors and variables can be unique to a specified field of endeavor, such as military security or epidemiology. The models can be ontological models. A rule set is then utilized to relate certain variables in the models to a specific event. The rule set can be embodied in a computer model, such as a Bayesian-Network. The system permits a user to query a knowledge store or database to acquire referent values for the rule set. Thereafter, the referent values are used to populate the rule set and compute the probability of the event occurring.

Abstract: A method for visualizing data comprises allocating the data to one or more groups. The method further comprises displaying one or more non-overlapping symbols on a map to represent the groups. The method further comprises merging the non-overlapping symbols into a lesser number of non-overlapping symbols when the map is zoomed out. The method also comprises splitting the one or more non-overlapping symbols into a greater number of non-overlapping symbols when the map is zoomed in.

Abstract: In one embodiment, searching documents includes generating a query conceptual graph for a search query. The query conceptual graph has graph terms representing concept types. A set of conceptually similar terms is identified for each graph term, and the set is mapped to the concept type represented by the graph term. Documents are searched in accordance with the set of conceptually similar terms. In another embodiment, selecting a document includes determining documents that satisfy a search query expressed as a query conceptual graph. A document conceptual graph that has graph terms representing concept types is generated for each document. A set of conceptually similar terms is identified for each graph term, where the set is mapped to the concept type represented by the graph term. A document is selected if terms of the sets associated with the document match terms associated with the query conceptual graph.

Abstract: According to one embodiment, categorizing concept types of a conceptual graph includes receiving the conceptual graph comprising one or more concept types, one or more relationship types, and one or more arcs. Each concept type is categorized according to the relationship types and the arcs. The categorization of the each concept type is recorded.

Abstract: According to one embodiment, attributes associated with a concept type of a query conceptual graph are received. A potentially conceptually similar term is received from an onomasticon. The potentially conceptually similar term is validated according to the attributes. According to another embodiment, attributes associated with a concept type of a document conceptual graph are received. A potentially conceptually similar term is received from an onomasticon. The potentially conceptually similar term is validated according to the attributes.

Abstract: A method for displaying geographical information comprises determining one or more geographical locations associated with a map. The method further comprises displaying information to a user regarding the one or more geographic locations, wherein the information displayed is retrieved from one or more databases. The method further comprises updating the information displayed if the user alters the one or more geographical locations on the map.

Abstract: A method for visualizing data comprises allocating the data to one or more groups. The method further comprises displaying one or more non-overlapping symbols on a map to represent the groups. The method further comprises merging the non-overlapping symbols into a lesser number of non-overlapping symbols when the map is zoomed out. The method also comprises splitting the one or more non-overlapping symbols into a greater number of non-overlapping symbols when the map is zoomed in.

Abstract: According to one embodiment of the disclosure, a method for information analysis comprises receiving a message in a database. The method further comprises identifying at least one of a person, place, or object within the received message. The method comprises obtaining information stored in one or more databases regarding the identified at least one person, place, or object. Finally, the method comprises displaying at least a portion of the information and the message for analysis.