MECHANISTIC CAUSAL REASONING FOR EFFICIENT ANALYTICS AND NATURAL LANGUAGE

摘要

A system and method for mechanistic causal reasoning are provided herein. The method includes receiving an input text from a user, the input text specified in a natural language. The method also includes building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains (e.g., causality, taxonomy, meronomy, time, space, identity, language, symbols and mathematical formulas). The method also includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences in natural language understanding, using the knowledge graph in conjunction with natural language understanding and logical inference. The method also includes generating a response to the input text, as to why and/or how unknown factors resulted in a known outcome, or what outcomes are likely given known causal factors.