The semantic network is an information model of any subject area. The network is built according to the type of directed graph, in which the vertices are objects of a given subject area, and the edges or arcs are the relations between objects. The role of objects can be concepts, properties, events, operations or processes, and the number of possible types of network relationships depends solely on its creator, based on the goals set. Thus, any semantic network is one of the most effective methods for representing knowledge. In addition, this is the most convenient form perceived by the human brain.
For the first time the phrase "semantic network" began to be used in the years 1968-1969 in the works of the American psychologist Quillian, who studied semantic memory. Later, this term began to be actively used in mathematics, computer science, and linguistics. Today, the presentation of information in the form of semantic networks is widespread in many fields of knowledge, there are a great many of them, and they all differ from each other in the number of types of relations, the number of arguments, and size.
If the number of relationship types is taken as the basis for classification, then each semantic network can be assigned either to a homogeneous or heterogeneous type. For homogeneous networks, the presence of only one type of relationship (arrow) is characteristic. Heterogeneous networks have two or more types of relationships. For practical purposes, the heterogeneous semantic network is of particular interest. An example of such a network is the structure of the famous Wikipedia Internet encyclopedia.
By the number of arguments, semantic networks are divided into binary and n-ary. Binary models demonstrate the relationship only between two objects (concepts) and are the simplest and most convenient for image in the form of a graph. Models of n-ary type are most often encountered in practice and are necessary to display the relationship between several objects.
And finally, in terms of size, any semantic network can be assigned either to small models that solve specific problems, or to industry-wide networks that serve as the basis for creating systems, or to a group of global models.
If we talk about exactly in which areas the semantic networks have received the greatest distribution today, then this is, of course, computer science. It is in this area of knowledge that the concept of network is used most often. As a rule, computer semantic networks are used for machine translation of languages. In this case, the translation process is carried out in two stages. At the first stage, the source text is translated into the so-called intermediate form, that is, its presentation in the form of a semantic network, and at the second stage, the resulting intermediate form is translated into the desired language.
For the effective implementation of networks today, specially developed network languages are used, such as Net and Simer + Mir. In addition, expert systems that use semantic models as a language for the visual representation of knowledge are widespread: Casnet, Prospector, Torus.