8. The system of claim 5 , wherein the post-processing utility when executed: (a) performs morphological normalization on template surfaces; (b) merges predicate distributions of templates sharing major semantic mappings that are proximate to one another based on term frequency inverse document frequency distance; and (c) applies matrix de-noising and correlation analysis techniques such as PCA and LSI on trained relationship path distributions for templates.
5. A computer system for automatically learning mappings from a natural language question to a structured query, comprising: (a) one or more computer systems, linked to a question answering system, the question answering system including: (i) a named entity recognizer for detecting one or more entity surfaces in input text so as to link a text phrase to a knowledge concept; (ii) a training utility for searching and organizing one or more mappings from a template surface to one or more relationship paths; and (iii) a training post-processing utility for normalizing and optimizing trained mapping distributions for one or more templates.