Such complex concepts as “thinking” and “consciousness”, and even more easily defined, such as “intelligence” and “knowledge”, are used by specialists of various profiles (for example, systems analysis, computer science, neuropsychology, psychology, philosophy, etc.) may vary significantly.
A complete, adequate representation of knowledge, which is perceived equally unambiguously by both people and machines, is the main problem of modern information exchange. Such information exchange is based on the system of concepts and relationships that make up knowledge.
Classification of knowledge
They can be classified into several categories: conceptual, constructive, procedural, factual, and metacognition.
- Conceptual knowledge is a set of specific concepts used in solving problems. They are often used in fundamental sciences and theoretical fields of science. In fact, conceptual knowledge constitutes the conceptual apparatus of science.
- Constructive knowledge - sets of structures, systems and subsystems, as well as interactions between them. Actively used in technology.
- Procedural knowledge - the methods and algorithms used in applied sciences most often.
- Factographic knowledge - the characteristics of objects and phenomena, both quantitative and qualitative. Used most often in experimental sciences.
- Metacognition - any knowledge about knowledge, their organization system, their engineering and about the order and rules of their application.
Knowledge organization
The knowledge organization system is the process of providing information in the form of messages that can be habitual (oral and written speech, drawings, etc.) and unusual (formulas, geographical map objects, radio waves, etc.).
In order for the knowledge organization system to be understandable and successful, it is necessary to use an understandable and constructive system of rules according to which knowledge will be presented and perceived. For this, a person uses language and writing.
Tongue
Language appeared and developed due to the fact that the knowledge accumulated by people constantly needs to be presented, expressed, stored and exchanged. A thought that cannot be expressed with a formal construction (language, image) loses the opportunity to become part of the information exchange. That is why throughout the history of mankind, language has been the most effective form of representing knowledge.
The richer the language, the more knowledge it expresses, respectively, making the culture of the people richer, which, in turn, allows you to develop new and more effective systems for organizing knowledge.
Language of science
The main problem in using language as a form of knowledge representation is the ambiguous semantic meaning of words and sentences. That is why the language of science plays a special role in the formalization of knowledge.
The main purpose of the language of science is to typify and standardize the forms of expression, compression and storage of knowledge. Using a standard, standard presentation of knowledge, you can get rid of the polysemy or semantic ambiguity of the language.
The fact that in natural conditions of language evolution makes a language richer (ambiguity of expressions), in the process of knowledge exchange becomes an obstacle, increasing the risk of misunderstanding, semantic noise and ambiguous perception of information.
Classification of knowledge
One of the main methods of formalizing knowledge is classification. This is the distribution of knowledge into groups in accordance with a particular class. That is, only information that meets certain criteria corresponding to the class gets into a certain class of knowledge.
Classification is a particularly important method of scientific systematics, which can not be avoided at the first stage of the formation of basic knowledge of a scientific direction. For example, in informatics without classification there is no equivalence that allows solving such important problems as comparison, search, and categorization. Without classification in science, we would not have such unique and invaluable data organization systems as the periodic table.
Knowledge Representation Models
Mendeleev’s table, Ranking table, Criminal code, family trees and other classification systems are models of knowledge representation. These are formal structures that interconnect certain knowledge: facts, phenomena, concepts, processes, objects, relationships.
For understanding and processing by the computer of knowledge about a separate subject area, this knowledge must be presented in a specific, formalized form. Depending on the purpose, computer processing of knowledge takes place in accordance with a model built on an algorithm. Accordingly, the knowledge presented in the model depends on the algorithm for their processing.
There are several models for representing knowledge in expert systems. The main ones are production, frame, network and logical.
Classification of models
The above models of the representation of knowledge, examples of which follow, though widespread, are far from the only ones. Today, there are many models that differ from each other in terms of validity, approaches to their creation and organization principles.
For example, the table below shows the types of models for representing knowledge, dividing them into empirical and theoretical, as well as further subdivision.
Empirical models | Theoretical models |
Product Models | Logical models |
Network models | Formal grammar |
Frame models | Combinatorial models |
Lenems | Algebraic models |
Neural networks |
Genetic Algorithms |
Empirical modeling
Empirical models of the organization and representation of knowledge are taken as an example of a person and try to embody the organization of his memory, consciousness and decision-making mechanisms and problem solving. Empirical modeling refers to any kind of model built on the basis of empirical observations, and not on relationships that can be mathematically described and modeled.
Empirical modeling is a generic term for knowledge representation models that are created from observations and experiments.
The empirical model operates according to a simple semantic principle: the creator observes the interaction of the model and its referent. The processing of the information obtained can be “empirical” in many ways, from analytical formulas, cause-effect relationships, to trial and error.
Knowledge Production Models
This data presentation model is most often based on relationships and causal relationships. If the information can be represented in the form of conditions such as "If <x>, Then <y>", then the model is production. It is most often used in applications and simple artificial intelligence.
The production models for the representation of knowledge are most often computer programs that provide a certain form of artificial intelligence with a number of rules of behavior, and also include the mechanism necessary to follow these rules under the given conditions.
A product (a set of rules) consists of two parts: a precondition (“IF”) and an action (“THAT”). If the precondition of the product corresponds to the current state of the world, then the model starts. The production model also contains a database, sometimes called working memory, which contains relevant knowledge.
The disadvantages of the production model are that with too many rules of action, the models may contradict each other.
Semantic Networks
They are based on the integrity of the image and are the most visible models of knowledge representation. The semantic network is most often represented in the form of a graph or a complex graph structure, the nodes or vertices of which are objects, concepts, phenomena, and the edges are the relationships between certain objects, concepts and phenomena.
The simplest semantic network can easily be represented in the form of a triangle, the vertices of which are such concepts as, say, “dog”, “mammal” and “spine”. In this case, the vertices will connect the sides of the triangle, which can be denoted by such connections and relations as “is,” “possesses,” “has y.” thus we get a knowledge representation model from which we learn that the dog is a mammal, mammals have a spine, and the dog has a spine.
Such models are illustrative, and with their help it is possible to most effectively imagine complex systems and cause-effect relationships. In addition, these semantic networks can be supplemented with new knowledge, expanding an existing network, that is, turn a triangle into a rectangle, then into a hexagon, and then into a complex network of intersecting figures, in which, for example, inheritance of properties can be observed.
Frame model
The frame model is named after the English word frame - frame or frame. A frame is a structure that collects data used to represent a specific concept.
As in sociology, where frames are a kind of stereotypical data that affect the human perception of the world and the decision-making process, in computer science and in working with artificial intelligence, frames are used to create structured data representing stereotypical situations. In fact, this is the initial, basic data system on which the perception of the world by artificial intelligence is based.
In addition, as effective models for representing knowledge, frames are active not only in computer science. Initially, they were a variation of semantic networks.
A frame consists of one or more slots. In turn, slots can themselves be frames. Thus, the frame model is able to represent complex conceptual objects, forming a wide hierarchical chain of knowledge.
The frame model of knowledge representation contains information on how to use the frame, what to expect during and after its use, and what to do when the expectations from using the frame are not met.
Certain types of data in the frame model are unchanged, while other data, usually stored in terminal slots, can change. Terminal slots are most often treated as variables. Slots and top-level frames carry information about the situation, which is always true, but terminal slots do not have to be true.
Frames of one complex network can be divided among themselves by slots of other frames of the same network.
The database can store prototype frames (immutable) and instance frames that are created situationally to represent a specific situation or a specific concept.
Frame models of knowledge representation are one of the most universal and are capable of displaying various types of knowledge:
- frame structures are used to represent concepts and objects;
- frame roles indicate role responsibilities;
- frame scripts describe behavior;
- frame situations are used to represent state and activities.
Neural networks
These algorithms can also be arbitrarily attached to a group of models based on an empirical approach to knowledge. In fact, neural networks are trying to copy processes that occur in the human brain. They are based on the theory that an artificial intelligence system with the same structures and processes as in the human brain will be able to get similar results in decision-making, assessing situations and perceiving reality.
Theoretically sound approach
Mathematical, predicative, and logical models for representing knowledge are based on this approach. These models guarantee the correctness of decisions, because they are based on formal logic. They are suitable for solving simple problems from a narrow subject area, often associated with formal logic.
Logical models of knowledge representation
This is one of the most popular models based on a theoretical approach. The logical model uses the predicate algebra, its system of axioms and inference rules. The most common logical models use terms - logical constants, functions and variables, as well as predicates, that is, expressions of logical actions.