Universal Semantic Code (USC)

This website is presenting Universal Semantic Code (USC) proposed and developed by Prof. Victor V. Martynov. USC is a Knowledge Representation and Inference Language with a long history. All articles here are about USC. They show how USC was growing and changing from version to version.

The chronography of the articles goes from the bottom to the top of the site excluding the article "Foundations of Semantic Coding" which gives the most essential explanation of USC basics.

Other articles demostrate how the USC theory may be applied. They repeat basic information but give different points of application and refine the USC theoretical basis. The evolution of USC is on its way and the website does not pretend on proving of a completeness of the theory. There are a lot of more USC publications in Russian. This website includes only publications in English.

Universal Semantic Code (USC) is a semantic language for knowledge representation. USC is based on a set of semantic primitives, which are normalized to unambiguously represent knowledge, and organized in a regular structure. The USC patterns have an interpretation within the formal algebra. The formal nature of USC along with regularity and unambiguous explicit expressions of the semantics allow us to describe virtually any domain. full article…

This paper is focused on the formal semantic model: Universal Semantic Code (USC), which acquires a semantic lexicon from thesauruses paired with their formal meaning representation. USC supports postulate: Knowledge Inference (KI) cannot be effective without semantic Knowledge Representation (KR); and proposes a computational model based (but not limited) on the formal representation of verb meanings. Such representation comprises meanings of verbs and phrasal verbs as main components of its semantic classification. The formal tools of USC provide verb meaning representation and natural language interpretation for semantic inference. A USC algebra defines semantic relations between verbs. full article…

The paper considers the method of technical terminology classification on the basis of terminological abstractions. The method is used to overcome jargon differences between scientific domains. Each scientific domain has its own terminology, or jargon. Within a domain, this is valuable as it provides conciseness and exactness. Outside the domain, however, it results in confusion. The experts within the domain can understand the same term in its different senses; to those outside the domain, it is unclear. The development of semantic classifiers of abstract terminology as intermediate links between various domains will help to solve this problem. This classifier may also serve as a form of ontology for the applicable domains. Such classifiers unite linguistic-semantic and functional approaches for analysing the terms in a natural language. The linguistic approach uses a natural language lexicon and considers the terms as parts of speech: e.g. as verbs, nouns, or adjectives. In the functional approach we consider a verb as an action, a noun as a subject or an object, and an adjective as an attribute of: the action, the subject or the object. full article…

The article covers problems of knowledge bases (KB) development and search automation of inventive solutions with intellectual computer systems. The following problems of the intellectual computer systems for inventive solution search are considered:

• correct inventive problem formulating;
• expanding a field of solution search by analogy on an abstract level for different domains;
• inventive solutions computing.

The article presents a design method of the inventive solutions semantic search on a basis of Universal Semantic Code (USC).
The article develops of Genrich Altshuller's problem formulating method in ARIZ and shows new search possibilities of the inventive solutions with semantic tools [1]. full article…

In the consciousness of a person thinking, understanding, knowledge are closely connected. The thinking does not come separately from the process of understanding and understanding is the purpose of thinking. In the same way knowledge is the result of understanding; all cognitive processes are closely interconnected. full article…

There are some Knowledge Representation (KR) models that have become classical: the logical, productional, frame models, the semantic network. The logical model is used to represent knowledge in calculus of first-order predicates and drawing conclusions by means of syllogism construction. In the productional models the knowledge is represented by the totality of rules like "if... then..." (the Phenomenon-Reaction). A frame is a structure of data (image) for representing a stereotyped situation. The information, belonging to the frame is contained in a slot (constituent of a frame). full article…

The paper gives the concept of Universal Semantic Code, which is considered as a knowledge representation language and task-solving algorithm in decision-making problems. The language of artificial intelligence presupposes an entirely canonized system of relations between semantics and syntax. The idea of canonization is based on the resolution of phrase ambiguity. In natural languages there two kinds of phrase ambiguity, one of which can be resolved, the other cannot. The first concerns traditional syntactic problems, the second deals with the new domain of semantic syntax. full article…