Logical analysis of natural language semantics to solve the problem of computer understanding
This addresses the challenge of natural language understanding for AI systems, but it appears incremental as it builds on existing logical and semantic frameworks.
The paper tackles the problem of computer understanding of natural language by proposing an object-oriented approach using a formal system based on predicative calculus and Horn's clauses, with sentences represented as semantic nets of typical predicates, which increases algorithm effectiveness and extends the class of solvable problems.
An object--oriented approach to create a natural language understanding system is considered. The understanding program is a formal system built on the base of predicative calculus. Horn's clauses are used as well--formed formulas. An inference is based on the principle of resolution. Sentences of natural language are represented in the view of typical predicate set. These predicates describe physical objects and processes, abstract objects, categories and semantic relations between objects. Predicates for concrete assertions are saved in a database. To describe the semantics of classes for physical objects, abstract concepts and processes, a knowledge base is applied. The proposed representation of natural language sentences is a semantic net. Nodes of such net are typical predicates. This approach is perspective as, firstly, such typification of nodes facilitates essentially forming of processing algorithms and object descriptions, secondly, the effectiveness of algorithms is increased (particularly for the great number of nodes), thirdly, to describe the semantics of words, encyclopedic knowledge is used, and this permits essentially to extend the class of solved problems.