1. A method of geodesic search for a semi-structured database and retrieval of relevant information therefrom based on a natural language query, comprising the steps of: parsing the natural language query to identify domain-specific semantic features of the natural language query; mapping the features to their semantic normalization stored in a symmetric reduction table; mapping the semantic normalization to rules in a knowledge base; retrieving information from the semi-structured database tagged with the semantic normalization based on the rules; measuring a degree of success of the step of retrieving; evolutionarily decomposing features stored in the symmetric reduction table that co-occur with rules that are improperly matched to retrieved information as determined by the step of measuring; reducing an arbitrary feature space derived from the step of parsing to a more salient feature space to reduce inclusion of extraneous features wherein the step of reducing comprises the steps of: comparing a context of the features parsed from the natural language query to cases stored in a case base to determine a metric match therefor; assigning a relative weighting factor to the features; calculating a score based on the metric match and the relative weighting factors; and selecting a case having the highest score for retrieving information from the semi-structured database; and replacing an existing rule in a case with a generated rule associated with a case when the total number of predictions “a” and the ratio “b” of the number of correct predictions divided by “a” are both non-decreasing.
1. A method of geodesic search for a semi-structured database and retrieval of relevant information therefrom based on a natural language query, comprising the steps of: parsing the natural language query to identify domain-specific semantic features of the natural language query; mapping the features to their semantic normalization stored in a symmetric reduction table; mapping the semantic normalization to rules in a knowledge base; retrieving information from the semi-structured database tagged with the semantic normalization based on the rules; measuring a degree of success of the step of retrieving; evolutionarily decomposing features stored in the symmetric reduction table that co-occur with rules that are improperly matched to retrieved information as determined by the step of measuring; reducing an arbitrary feature space derived from the step of parsing to a more salient feature space to reduce inclusion of extraneous features wherein the step of reducing comprises the steps of: comparing a context of the features parsed from the natural language query to cases stored in a case base to determine a metric match therefor; assigning a relative weighting factor to the features; calculating a score based on the metric match and the relative weighting factors; and selecting a case having the highest score for retrieving information from the semi-structured database; and replacing an existing rule in a case with a generated rule associated with a case when the total number of predictions “a” and the ratio “b” of the number of correct predictions divided by “a” are both non-decreasing. 3. The method of claim 1 , further comprising the step of randomly searching the symmetric reduction table during the step of parsing to improve feature identification.