Logical analysis of natural language semantics to solve the problem of computer understanding

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.

Inference and Plausible Reasoning in a Natural Language Understanding System Based on Object-Oriented Semantics

Algorithms of inference in a computer system oriented to input and semantic processing of text information are presented. Such inference is necessary for logical questions when the direct comparison of objects from a question and database can not give a result. The following classes of problems are considered: a check of hypotheses for persons and non-typical actions, the determination of persons and circumstances for non-typical actions, planning actions, the determination of event cause and state of persons. To form an answer both deduction and plausible reasoning are used. As a knowledge domain under consideration is social behavior of persons, plausible reasoning is based on laws of social psychology. Proposed algorithms of inference and plausible reasoning can be realized in computer systems closely connected with text processing (criminology, operation of business, medicine, document systems).

Question Answering in a Natural Language Understanding System Based on Object-Oriented Semantics

Algorithms of question answering in a computer system oriented on input and logical processing of text information are presented. A knowledge domain under consideration is social behavior of a person. A database of the system includes an internal representation of natural language sentences and supplemental information. The answer {\it Yes} or {\it No} is formed for a general question. A special question containing an interrogative word or group of interrogative words permits to find a subject, object, place, time, cause, purpose and way of action or event. Answer generation is based on identification algorithms of persons, organizations, machines, things, places, and times. Proposed algorithms of question answering can be realized in information systems closely connected with text processing (criminology, operation of business, medicine, document systems).

Object-oriented semantics of English in natural language understanding system

A new approach to the problem of natural language understanding is proposed. The knowledge domain under consideration is the social behavior of people. English sentences are translated into set of predicates of a semantic database, which describe persons, occupations, organizations, projects, actions, events, messages, machines, things, animals, location and time of actions, relations between objects, thoughts, cause-and-effect relations, abstract objects. There is a knowledge base containing the description of semantics of objects (functions and structure), actions (motives and causes), and operations.