This invention discloses an
artificial intelligence-based
knowledge graph question-answering
interaction method and
system, belonging to the field of
knowledge graph technology. It includes four steps: temporal knowledge validity screening, reasoning path
quality assessment, causal attribute consistency
verification, and reliable result output and closed-
loop optimization. After receiving a user's
natural language query, the
system first extracts the query time interval, filters valid triples matching the time dimension in the
knowledge graph, calculates the path time matching degree and candidate answer attribute matching degree based on the valid triples, and then calculates the credibility after fusion before outputting the qualified answer. This invention integrates multiple technologies, using dual
verification to ensure the accuracy of the answer in terms of temporality and logic, reducing errors. The
system continuously learns and improves based on application feedback, enhancing the accuracy of the knowledge graph and the quality of
question answering. Furthermore, the data interaction between modules is standardized and logically clear, enabling flexible responses to various queries and providing users with traceable, high-quality answers to meet diverse needs.