Abstract:
A method, machine readable storage medium, and system for providing a self learning semantic search engine. A semantic network may be set up with initial configuration. A search engine coupled to the semantic network may build indexes and semantic indexes. A user request for business data may be received. The search engine may be accessed via a semantic dispatcher. And based on the access, search engine may update the indexes and semantic indexes.
Abstract:
A semantic phrase suggestion engine that provides term and sentence suggestions based on context-specific user groups. Knowledge domains within a semantic network may be automatically derived from user software applications, and each term within the knowledge domain includes meta-data about the terms, e.g., term type and an importance indicator. The indicators may be defined within the context of specific user groups and relate to how many times that group has used the term (e.g., in documents, emails, etc.) The semantic phrase suggestion engine may also include spelling conditions and grammar conditions, which can then provide phrase suggestions according to the conditions and importance indicators, specific to a user group.
Abstract:
A method, machine readable storage medium, and system for providing a self learning semantic search engine. A semantic network may be set up with initial configuration. A search engine coupled to the semantic network may build indexes and semantic indexes. A user request for business data may be received. The search engine may be accessed via a semantic dispatcher. And based on the access, search engine may update the indexes and semantic indexes.
Abstract:
A semantic phrase suggestion engine that provides term and sentence suggestions based on context-specific user groups. Knowledge domains within a semantic network may be automatically derived from user software applications, and each term within the knowledge domain includes meta-data about the terms, e.g., term type and an importance indicator. The indicators may be defined within the context of specific user groups and relate to how many times that group has used the term (e.g., in documents, emails, etc.) The semantic phrase suggestion engine may also include spelling conditions and grammar conditions, which can then provide phrase suggestions according to the conditions and importance indicators, specific to a user group.
Abstract:
A method, machine readable storage medium, and system for providing personalized semantic controls for semantic systems. A semantic network may be set up with initial configuration. A business application user interface, including semantic controls, may be coupled to the semantic network to interact with the semantic network. Semantic objects and relations may be defined in the semantic network for business terminology. A user request for business data may be received. The semantic network may update the objects and relations for business terminology based on the request. The business application user interface may provide for personalized semantic controls based on the updated objects and relations.
Abstract:
A cascading learning system for semantic search is described including the generation, training and testing of a domain-specific module for a domain-specific search. One or more input elements and output elements are specified for the domain-specific module with reference to a domain that relates these elements together through data sets that include related metadata. The related metadata may include semantic terms that are incorporated into a contextual network applicable to the domain.
Abstract:
A cascading learning system as a normalized semantic search is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application. The request analyzer has a normalization manager, a semantic parser, and a context builder. The normalization manager normalizes the search terms and generates a normalized semantic request based on a context. The request dispatcher and classifier classifies and dispatches the normalized semantic request to a corresponding domain-specific module that generates a prediction with a trained probability of an expected output. The terminology manager receives the normalized semantic request from the request dispatcher and classifier, and manages terminology stored in a contextual network.
Abstract:
Data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes series time-stamped frames for each of the sensors and at least one of the sources has two or more associated sensors. The received data can be sorted and processed, for each sensor on a sensor-by-sensor basis, using a sliding window. The sorted and processed data can then be correlated and written into a data storage application. Related apparatus, systems, techniques and articles are also described.
Abstract:
Data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes series time-stamped frames for each of the sensors and at least one of the sources has two or more associated sensors. The received data can be sorted and processed, for each sensor on a sensor-by-sensor basis, using a sliding window. The sorted and processed data can then be correlated and written into a data storage application. Related apparatus, systems, techniques and articles are also described.