Abstract: A method of semantic searching. The method may include receiving a first search query, obtaining a disambiguation term for semantically disambiguating the first search query, and creating, with a processor, a second search query based at least in part on the first search query and the disambiguation term. The method may also include at least one of outputting search results obtained from a search conducted based at least in part on the second search query and sending the second search query to a search service for outputting search results.

Abstract: Computer-implemented systems and methods for synthesis of concept definitions and concept relationships from a domain of data, utilizing different semantic processing protocols such as formal concept analysis and faceted classification synthesis from existing domain concepts that have a confidence gradient built into them. A cognitive or an input agent provides an input of an active concept which is matched against existing domain concepts. The resultant pool of relevant domain concepts is then used to derive virtual concept definitions using a semantic processing protocol. The derivation is then overlaid with a concept of relative proximity of an attribute from another within an attribute set. An additional layer of coherence is given by the relative proximity measure. The end result is a pool of related virtual concept definitions in a tree structure.

Abstract: The present invention provides a computer network implementable integration of promoted information with non-promoted content before a consumer interaction with the network, such that when the consumer interacts with the network, a consumer information interacts with this integrated content and delivers a message to the consumer such that the consumer visualizes this message as part of consumer experience without distracting from the network interaction task at hand. The integration is facilitated by semantic analysis and synthesis to naturally position the promoted content as close to the consumer interaction tasks as possible at that instant. This approach displaces the current practise of matching message to media placement while further enabling a promoter to evaluate and respond to feedback data depicting the efficacy of the sponsor message. The network in question is any computer network such as the Internet or intranet.

Abstract: A computer network-implemented method for displaying a tag cloud representing a data structure. The data structure includes data entities and representations of relationships between the data entities. The method enables one or more users to interact with the data structure, and via the data structure, to interact with one another. In an example embodiment, the method comprises, via a computer processor, populating a display of a user interface showing a tag cloud populated with images of data entities from the data structure, such images, and in response to user input to the interface, associating one or more visual properties of the user interface with one or more of said relationships between data entities in the data structure.

Abstract: Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including both an elemental data structure and knowledge processing rules stored as machine-readable data and/or programming instructions. One or more of the knowledge processing rules may be applied to analyze an input complex KR to deconstruct its complex concepts and/or concept relationships to elemental concepts and/or concept relationships to be included in the elemental data structure. One or more of the knowledge processing rules may be applied to synthesize an output complex KR from the stored elemental data structure in accordance with context information. Methods of populating an elemental data structure and methods of synthesizing complex KRs from the elemental data structure may rely on linguistic inference rules and/or elemental inference rules.

Abstract: Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including both an elemental data structure and knowledge processing rules stored as machine-readable data and/or programming instructions. One or more of the knowledge processing rules may be applied to analyze an input complex KR to deconstruct its complex concepts and/or concept relationships to elemental concepts and/or concept relationships to be included in the elemental data structure. One or more of the knowledge processing rules may be applied to synthesize an output complex KR from the stored elemental data structure in accordance with an input context. Multiple input complex KRs of various types may be analyzed and deconstructed to populate the elemental data structure, and input complex KRs may be transformed through the elemental data structure to output complex KRs of different types, providing semantic interoperability to KRs of different types and/or KR models.

Abstract: Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information.

Abstract: Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information.

Abstract: Techniques for customizing knowledge representation systems including identifying, based on a plurality of concepts in a knowledge representation (KR), a group of one or more concepts relevant to user context information, and providing the identified group of one more concepts to a user. The KR may include a combination of modules. The modules may include a kernel and a customized module customized for the user. The kernel may accessible via a second KR.

Abstract: Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information.

Abstract: A method of semantic searching. The method may include receiving a first search query, obtaining a disambiguation term for semantically disambiguating the first search query, and creating, with a processor, a second search query based at least in part on the first search query and the disambiguation term. The method may also include at least one of outputting search results obtained from a search conducted based at least in part on the second search query and sending the second search query to a search service for outputting search results.

Abstract: A computer network-implemented method for displaying a tag cloud representing a data structure. The data structure includes data entities and representations of relationships between the data entities. The method enables one or more users to interact with the data structure, and via the data structure, to interact with one another. In an example embodiment, the method comprises, via a computer processor, populating a display of a user interface showing a tag cloud populated with images of data entities from the data structure, such images, and in response to user input to the interface, associating one or more visual properties of the user interface with one or more of said relationships between data entities in the data structure.

Abstract: Methods and system for providing information selected from a large set of digital content to a user. Techniques include receiving user context information associated with the user and identifying or generating a first concept in a semantic network, the first concept representing at least a portion of the user context information. The method further comprises obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and providing information to the user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept, wherein the first concept in a semantic network is represented by a data structure storing any data associated with a node in the semantic network.

Abstract: Computer-implemented systems and methods for synthesis of concept definitions and concept relationships from a domain of data, utilizing different semantic processing protocols such as formal concept analysis and faceted classification synthesis from existing domain concepts that have a confidence gradient built into them. A cognitive or an input agent provides an input of an active concept which is matched against existing domain concepts. The resultant pool of relevant domain concepts is then used to derive virtual concept definitions using a semantic processing protocol. The derivation is then overlaid with a concept of relative proximity of an attribute from another within an attribute set. An additional layer of coherence is given by the relative proximity measure. The end result is a pool of related virtual concept definitions in a tree structure.

Abstract: The present invention provides a computer network implementable integration of promoted information with non-promoted content before a consumer interaction with the network, such that when the consumer interacts with the network, a consumer information interacts with this integrated content and delivers a message to the consumer such that the consumer visualizes this message as part of consumer experience without distracting from the network interaction task at hand. The integration is facilitated by semantic analysis and synthesis to naturally position the promoted content as close to the consumer interaction tasks as possible at that instant. This approach displaces the current practise of matching message to media placement while further enabling a promoter to evaluate and respond to feedback data depicting the efficacy of the sponsor message. The network in question is any computer network such as the Internet or intranet.

Abstract: Techniques for customizing knowledge representation systems including identifying, based on a plurality of concepts in a knowledge representation (KR), a group of one or more concepts relevant to user context information, and providing the identified group of one more concepts to a user. The KR may include a combination of modules. The modules may include a kernel and a customized module customized for the user. The kernel may accessible via a second KR.

Abstract: A computer network-implemented method for displaying a tag cloud representing a data structure. The data structure includes data entities and representations of relationships between the data entities. The method enables one or more users to interact with the data structure, and via the data structure, to interact with one another. In an example embodiment, the method comprises, via a computer processor, populating a display of a user interface showing a tag cloud populated with images of data entities from the data structure, such images, and in response to user input to the interface, associating one or more visual properties of the user interface with one or more of said relationships between data entities in the data structure.

Abstract: Techniques for customizing knowledge representation systems including identifying, based on a plurality of concepts in a knowledge representation (KR), a group of one or more concepts relevant to user context information, and providing the identified group of one more concepts to a user. The KR may include a combination of modules. The modules may include a kernel and a customized module customized for the user. The kernel may accessible via a second KR.

Abstract: Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including an elemental data structure and knowledge processing rules that are machine-readable. The elemental data structure may include a universal kernel and customized modules, which may represent knowledge that is generally applicable to a population and knowledge that is specifically applicable to individual data consumers, respectively. A method of constructing an elemental data structure may include analyzing first information to identify a first elemental component associated with a data consumer, and adding the first elemental component to a customized module corresponding to the data consumer. The method may also include analyzing second information to identify a second elemental component associated with a population, and adding the second elemental component to the universal kernel.

Abstract: Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including both an elemental data structure and knowledge processing rules stored as machine-readable data and/or programming instructions. One or more of the knowledge processing rules may be applied to analyze an input complex KR to deconstruct its complex concepts and/or concept relationships to elemental concepts and/or concept relationships to be included in the elemental data structure. One or more of the knowledge processing rules may be applied to synthesize an output complex KR from the stored elemental data structure in accordance with an input context. Multiple input complex KRs of various types may be analyzed and deconstructed to populate the elemental data structure, and input complex KRs may be transformed through the elemental data structure to output complex KRs of different types, providing semantic interoperability to KRs of different types and/or KR models.