TOPOLOGICAL METHODS TO ORGANIZE SEMANTIC NETWORK DATA FLOWS FOR CONVERSATIONAL APPLICATIONS

摘要

A system and methods for enforcing uniform branching of node-to-node inheritance links within semantic networks, to control data flows to and from such networks in conversational applications. Enforced uniform branching criteria converge the population of directly connected nodes of each node toward a small system-wide constant, and converge each sibling inheritance node to a similar level of abstractness, and are also used to select the best candidate tree from a set of competing representation trees within the semantic network. Uniform branching criteria are applied to competing trees for speech recognition, for object recognition in vision systems, for concept recognition in text scanning systems, and for algorithm definition. For speech recognition, phonemes are identified and matched to dictionary nodes in the semantic network. For visual object recognition, object features are identified and matched. For text scanning, words are identified and matched. For speech, visual and text the sets of competing representation trees are formed from alternative combinations of matched dictionary nodes. For algorithms, competing sets consist of alternative trees of commands. Conversational data flows are directed into active conversations until those conversations comprise a targeted number of inheritor nodes. Active memory space reserves are reclaimed by archiving nodes. Conversational emotional states are categorized by shifts in node topologies of active conversations, to verify that conversations have successfully communicated information. To gather and disseminate information across distributed computer networks, conversational questions are forwarded and conversational answers gathered by moderator computers representing their network-subnet's computers.