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Abstract

A computer implemented conversational method to parse meaning from input sequences of symbols, using a large dictionary stored in a semantic network, where thousands of dictionary entries for symbols are stored as nodes connected by inheritance links. Input symbols are matched to dictionary entries, with priority given to entries which cover a range of concreteness similar to the range covered by the dictionary. Ranges of concreteness which are missing are identified and reported back to the user. Concreteness of each entry is quantified from topology of inheritance links; quantification is done automatically when defining meanings by inheritance. As network topology shifts due to creation and removal inheritance links, quantified concreteness of affected dictionary entries are updated, automatically maintaining a consistent concreteness numbers. New inheritor links cause entries to become more concrete. When they become too concrete to be inherited from, indirect links via entries of lesser concreteness are substituted for the direct links, thus maintaining a uniform and consistent inheritance topology within the semantic network. Variations in dictionary semantics emerging between authors or evolving over time are automatically cohered into a single consistent inheritance topology, by shifting the inheritance links so that concrete entries always inherit from more abstract entries. When parsing text, the accuracy of the method of Most Specific Common Abstraction is improved by the consistency and uniformity of inheritance enforced by the shifting of inheritance links to conform to a consistent topology of inheritance.

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First Claim