Geodesic search and retrieval system and method of semi-structured databases
First Claim
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.
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Abstract
A knowledge-based decision support system that allows for communication and learning to occur using natural language is presented. The system has a capability to automatically extract features from the natural language using symmetric reductions and random search. The iterative generalization of the rule base and checking of the resultant base against a case base from which the generalizations are induced is also provided. The decision support system can be used to search semi-structured databases and automatically learns new knowledge and search control knowledge where it is most needed based on the pattern of previous rule firings.
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Citations
13 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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Specification