System and method using global and local-aware denoising framework for commonsense knowledge graph noise detection

A method using global and local-aware denoising framework for commonsense knowledge graph noise detection, comprising:

• receiving, by an input receiver serving as a user interface, user inputs to process the user inputs, so as to obtain commonsense knowledge graph (CSKG) information;
• receiving, by a triple encoder, the CSKG information from the input receiver;
• extracting, by the triple encoder, semantic information from free-text formatted nodes in the CSKG information, so as to obtain structured triples;
• receiving, by a global noise detector, the structured triples output from the triple encoder;
• analyzing and detecting, by the global noise detector, noise at a global level from the CSKG information, wherein the global noise detector identifies high-frequency patterns through rule mining and uses a rule encoder to generalize learned rules and guide noise detection;
• receiving, by a local noise detector, the structured triples output from the triple encoder;
• analyzing and detecting, by the local noise detector, noise at a local level from the CSKG information, wherein the local noise detector utilizes the structured triples output by the triple encoder to aggregate neighboring triple information around both the head and tail nodes via graph networks;
• providing, by a comprehensive evaluation scorer, a comprehensive score based on global noise detection and local noise detection from the global noise detector and the local noise detector, respectively, so as to indicate a noise level for the CSKG information.