What is the difference between knowledge and data? How to gain knowledge

Module 1 (1.5 credits): Introduction to Economic Informatics

Topic 1.1: Theoretical foundations of economic informatics

Topic 1.2: Technical means of information processing

Topic 1.3: System Software

Topic 1.4: Service software and algorithmic basics

Economic informatics and information

1.1. Theoretical foundations of economic informatics

1.1.2. Data, information and knowledge

Basic concepts of data, information, knowledge.

TO basic concepts that are used in economic informatics include: data, information and knowledge. These concepts are often used interchangeably, but there are fundamental differences between these concepts.

The term data comes from the word data - fact, and information (informatio) means explanation, presentation, i.e. information or message.

Data is a collection of information recorded on a specific medium in a form suitable for permanent storage, transmission and processing. Transformation and processing of data allows you to obtain information.

Information is the result of data transformation and analysis. The difference between information and data is that data is fixed information about events and phenomena that is stored on certain media, and information appears as a result of data processing when making decisions specific tasks. For example, various data are stored in databases, and upon a certain request, the database management system provides the required information.

There are other definitions of information, for example, information is information about objects and phenomena environment, their parameters, properties and condition, which reduce the degree of uncertainty and incomplete knowledge about them.

Knowledge– this is recorded and practice-tested processed information that has been used and can be repeatedly used for decision-making.

Knowledge is a type of information that is stored in a knowledge base and reflects the knowledge of a specialist in a specific subject area. Knowledge is intellectual capital.

Formal knowledge can be in the form of documents (standards, regulations) regulating decision-making or textbooks, instructions describing how to solve problems.

Informal knowledge is the knowledge and experience of specialists in a certain subject area.

It should be noted that there are no universal definitions of these concepts (data, information, knowledge), they are interpreted differently.

Decisions are made based on the information received and existing knowledge.

Decision making- this is the choice of the best, in a certain sense, solution option from a set of acceptable ones based on the available information.

The relationship between data, information and knowledge in the decision-making process is presented in the figure.

Rice. 1.

To solve the problem, fixed data is processed on the basis of existing knowledge, then the information received is analyzed using existing knowledge. Based on the analysis, all feasible solutions are proposed, and as a result of the choice, one decision that is best in some sense is made. The results of the solution add to knowledge.

Depending on the scope of use, information can be different: scientific, technical, management, economic, etc. Economic information is of interest to economic informatics.

Classification of knowledge

Interpretations of knowledge

Knowledge representation

Topic 1. The concept of knowledge

Knowledge– this is a practice-tested result of knowledge of reality, a reflection in the human mind.

Knowledge– laws of the subject area (principles, connections, laws) obtained as a result of practical activities and professional experience, allowing specialists to solve problems in this area.

Knowledge is the result obtained by knowledge.

Knowledge– this is formalized information that is referred to when making various conclusions based on available data using logical inferences.

Knowledge refers to information stored in a computer, formalized in accordance with structural rules, which can be used to solve problems.

· Psychological: Knowledge – psychological images or mental models.

· Intelligent: Knowledge is a set of information about a certain subject area, including facts about objects of the subject area, about the properties of the object, and the relationships connecting them, descriptions of processes occurring in a given subject area and containing information about the solution typical tasks.

· Formally – logical: Knowledge is formalized information used to obtain or derive new knowledge using specialized procedures.

· Informational-technological: Knowledge is structured information stored in computer memory and used in the operation of intelligent systems.

1. Depending on the source:

a. a priori

b. accumulated

i. expert

ii. observed

iii. output

2. Depending on the nature of use when solving problems:

a. declarative

b. procedural

c. metaknowledge

3. Depending on the degree of reliability:

a. clear knowledge

b. fuzzy knowledge

4. Depending on the depth:

i. superficial:

b. knowledge-copy

c. knowledge-acquaintances

i. deep:

1.1. A priori - are put into the knowledge base before the start of the functioning of the information information system that includes this knowledge base. In addition, when working with a knowledge base, the reliability of the a priori knowledge contained in it is not overestimated.

1.2. Accumulated knowledge is formed during the operation of the knowledge base. The sources of this knowledge can be experts (experts), external artificial devices observers (observed), rules and procedures for inference and verification of knowledge operating within intelligent system(outputable).

2.2. Procedural knowledge is information about ways to solve typical problems in a certain subject area.

2.3. Metaknowledge – knowledge about knowledge that will hold back general information about the principles of using knowledge. The level of metaknowledge also includes the strategy for managing the selection and application of procedural knowledge.

3. The classification of knowledge depending on the degree of its reliability is based on the so-called. non-factors inherent in knowledge: incomplete information about the fragment of the subject area under consideration is the inaccuracy of quantitative and qualitative assessments, ambiguity of the rules for deducing new knowledge, inconsistency of some provisions in the knowledge base.

4. Superficial – knowledge about the visible relationships of objects and phenomena. Deep knowledge is based on abstract analogies that make it possible to explain the essence of phenomena.

Knowledge representation– expression for k\l formal language properties various objects and patterns essential for solving problems.

Main areas of research related to knowledge representation:

· development of a methodology for constructing problem-oriented mathematical models;

· development of a formal apparatus for describing such models;

· development of theories of calculations in such models;

· development of technologies for implementing software support for such models.

When developing a knowledge representation model, questions may be asked: “What to represent?” and “How to present?”

The first issue concerns the organization or choice of knowledge structure.

The second is related to the representation of knowledge in the chosen structure.

The composition of the knowledge of the information system depends on the subject area, the requirements and goals of the user, and the purpose of the system structure. When developing almost any MIS, you need to have the following minimum set knowledge:

· knowledge about the problem solving process;

· knowledge of the language of communication and ways of organizing dialogue between the system and the user;

· knowledge about the problem area and knowledge about ways of representing and modifying knowledge.

Data call information of a factual nature that describes objects, processes and phenomena of the subject area, as well as their properties.

Knowledge are a more complex category compared to data. Knowledge describes not only individual facts, but also the relationships between them, which is why knowledge is sometimes called structured data. Knowledge is the result of a person’s mental activity aimed at generalizing his experience gained as a result of practical activity.

Knowledge is obtained as a result of applying certain processing methods to the source data and connecting external procedures.

DATA + PROCESSING PROCEDURE = INFORMATION

INFORMATION + PROCESSING PROCEDURE = KNOWLEDGE

Feature knowledge is that it is not contained in original system. Knowledge arises as a result of comparing information units, finding and resolving contradictions between them, i.e. knowledge is active; its appearance or shortage leads to the implementation of certain actions or the emergence of new knowledge. Knowledge differs from data by having the following properties.

1. Internal interpretation – independence of knowledge from the interpreting program, the ability to answer questions regarding the contents of memory. It allows you to correlate data stored in memory with its semantic content. Its presence makes it possible to construct procedures that answer human questions about the contents of memory on behalf of the computer.

2, 3. Availability of internal and external knowledge structures. Extending the principle of dividing objects into already identified components of the whole makes it possible to build multi-level hierarchical representations. Part objects can be interpreted independently of each other, i.e. as elements of a set. If the relationship individual elements parts is essential, it must be reflected in the knowledge base. On a variety of objects of the subject area, both whole and their parts, various semantic relations (generic relations, temporal, spatial) are introduced that describe the structure of a fragment of the subject area. Such a structural representation of the subject area is very important aspect knowledge, because the principles of decomposing objects in a subject area and identifying the system of relations between them are based on similar mechanisms of human thinking.

4. Scaling. Allows you to compare and organize qualitatively identical, but quantitatively different properties and relationships of objects in the subject area. In human memory, knowledge about the world around us is ordered, which is determined by various scales. The scale is a sequence of marks, each of which is associated with an assessment value or the value of a certain quantity. The following types of scales are distinguished: 1) Metric, which are divided into Absolute and Relative; 2) Ordinal scales, which are divided into linguistic and oppositional. In metric scales, by the location of the points, you can determine the degree of difference of the corresponding information units. Using metric scales, you can establish quantitative relationships and the order of certain estimates or quantities. In absolute metric scales, the origin never changes. In relative scales, the origin varies in each case and is determined by the situation or the current moment time. IN ordinal scales fix the order of information units in linguistic Ordinal scales use quantifiers that serve to introduce quantitative or qualitative measures. Such quantifiers are never, very rarely, rarely, often, etc. IN oppositional ordinal scales the ends of the scale correspond to extreme or incompatible properties and relationships of objects, which are indicated by pairs of antonyms, the middle position is considered neutral. Examples of such antonyms are the following pairs: slow - fast, strong - weak. The scales are specified by three parameters.

Knowledge in modern companies

Xerox company in recent years does not position itself as a manufacturer photocopiers, but as a document processing company. The ZM company calls itself an innovative problem solving company. IBM identifies itself as a company that creates long-term economic benefits for customers by combining its business knowledge with broad technological capabilities. Office equipment company Steelcase says it sells proprietary knowledge and services that help create best conditions people staying at their workplaces. What adds value to all these companies? These are mainly knowledge-based decisions: technical and technological know-how, product design, marketing research, identifying the true needs of customers. It is knowledge that gives these companies a sustainable competitive advantage.

Let's consider the difference between knowledge and data and information. Managers begin to realize that these are different things especially clearly after the organization has spent significant funds to create one or another database or information system, or simply spent these funds on computerization, without any corresponding effect.

Data- is a collection of different objective facts. In corporations, this is, for example, structured records of transactions (in particular, data on all sales: how many, when and who bought, how much and when they paid, etc.). This data does not tell us why the buyer came here and whether he will come again.

Information is a hierarchical collection of data about certain aspects real world. Information is a flow of messages, and knowledge is created from this flow; it depends on the opinions and beliefs of the knowledge bearer.

Information is a kind of message, usually in the form of a document or in video or audio form. It has a recipient and a sender. It informs, i.e. "gives shape" to the recipient by changing his evaluations or behavior. The extent to which the message is information is determined by the recipient. It is he who evaluates how much the received message informs him, and how much it is simply information noise.

Data turns into information in several ways:

o contextualization: we know what this data is for;
o categorization: We break down data into types and components;
o count: we process data mathematically;
o correction: we correct errors and eliminate omissions;
o compression: we compress, concentrate, aggregate data.

Knowledge- a concept deeper and broader than just data or information. Each enterprise, in the course of its activities, collects data, structures it and generates new knowledge. Most often this knowledge concerns technology, if we're talking about O material production, as well as technologies for working with clients and technologies for interacting with each other, if we are talking about an enterprise providing customer service. It can also be knowledge regarding the environment of the enterprise - about demographic, macroeconomic, social, macroeconomic, technological and market trends.

The difference between knowledge and information and data: an example

Chrysler has a meeting computer files, which are called the "Book of Engineering Knowledge" and represent comprehensive data and information about the creation of cars of this company, which can be used by every developer of new cars. When the manager received data on the crash tests performed, he refused to put them in files without appropriate processing. He suggested answering the following questions:

o why these tests were carried out;
o what are the results compared to other similar tests of this company from other years and competitors;
o what are the conclusions and tests for the design of the car and its main components?

Similar questions transform information into knowledge; Moreover, the answers to these questions add value to the information, or, in other words, add value. In practice, there are opposite examples when, by adding unnecessary, empty information, the original information loses its value. There is a loss of value due to erosion necessary information in the flow of information noise.

Knowledge is a combination of experience, values, contextual information, expert assessments, which provides a general framework for assessing and incorporating new experiences and information. Knowledge exists in the minds of those who know. In organizations, it is recorded not only in documents, but also in processes, procedures, norms, and in practice in general.

Just as information arises from data, so knowledge arises from information by:

o comparisons, determining the scope (how and when we can apply information about this phenomenon to another, similar one);
o establishing connections (how this information relates to other information);
o assessments (how can one assess this information and how others evaluate it);
o determining the scope (how this information applies to certain decisions or actions).

The process of transforming data into information, and information into knowledge is shown in Fig. 14.1.

Rice. 14.1.

There is a distinction between individual and group knowledge. Traditional views assume that knowledge is the prerogative of individuals, with a group being just the simple sum of the members of that group, and group knowledge being the sum of their knowledge.

There is another, modern point of view, according to which a group of people forms a new entity with its own unique specificity. Within the framework of this concept, we can talk about group behavior and group knowledge, respectively. This new concept is widely used within the science of knowledge management. Thus, knowledge can be not only individual, but also among a group of people. Then they say that the organization as a whole knows something, a group, a brigade, etc. knows something.

Bill Gates, in his book Business at the Speed of Thought, writes about the need to increase corporate IQ. By this, he means not only the number of smart employees, but also the accumulation of knowledge in the company as a whole and the free flow of information, which allows employees to benefit from each other's ideas.

Knowledge can be explicit or tacit. Explicit knowledge can be expressed in words and numbers and can be transmitted in formalized form on media. This refers to those types of knowledge that are transmitted in the form of prescriptions, instructions, books, on various media, in the form of memos, etc.

Tacit knowledge in principle, it is not formalized and can only exist together with its owner - a person or a group of persons.

There are two types of tacit knowledge. The first is the technical skills that are demonstrated by masters of their craft and are, as a rule, the result of many years of practice. The second is the beliefs, ideals, values and mental models that we use without thinking about them.

Tacit knowledge is formed and developed in the process of creating and strengthening a positive corporate culture and through group interaction tools (retreats, creative groups etc.).

The attitude towards explicit and tacit knowledge on the part of business firms is very contradictory. On the one hand, many firms strive to transform tacit knowledge into explicit knowledge. This is done in order, on the one hand, not to depend on individuals, and on the other, to duplicate significant achievements. At the same time, these firms are not interested in seeing their core competitive advantages transferred into a form ready for duplication. This is why many companies try to retain some of their competitive advantages in those forms that cannot be duplicated (specific trainings, corporate culture, special systems service, etc.).

The bearer of both explicit and implicit knowledge can be not only a specific person, but also an organization. Consequently, we can talk about tacit group knowledge, which underlies stable patterns of collective reactions and internal interactions.

In Western literature, the term “routines” is sometimes used to denote tacit group knowledge, which are repetitive actions, regular behavioral patterns of an organization or firm. Routines are what happen automatically, without instructions and in the absence of a choice procedure; however, routines cannot be codified.

In Russian, routine means routine, established practice, specific mode, pattern, established rules regarding people's activities. At the same time, the concept of “routine” has one more meaning: it is an inert order, i.e. an order that gravitates towards the old, familiar, and, due to its backwardness, is impervious to the new, progressive. In cases where the term "routine" is used to denote group tacit knowledge, then the connotations related to rigidity are absent.

Thus, personal tacit knowledge is, first of all, skills. At the same time, group tacit knowledge is, first of all, routines. Routines do not exist in isolation, but form interdependence. Some routines may be implicit for some members of a group (organization) and explicit for others. Thus, the boundaries between explicit and implicit knowledge are relative, and we can also talk about the degree of tacitness of this knowledge. The ratio of explicit and implicit, individual and group knowledge is presented in Table. 14.1.

Table 14.1

Knowledge ratio

The presence of tacit knowledge in an organization forces an approach to knowledge management in an unconventional way. Traditionally, knowledge management refers to the creation, development and use various bases data and knowledge. The presence of tacit knowledge shifts attention to the means of direct communication between people. It is important not only and not so much to create corporate encyclopedia, which records everything that any of the employees knew and encountered. In the case of tacit knowledge, it is more important to have at hand the coordinates of people who know the recipe and have relevant experience, to create a culture of communication using brainstorming sessions, meetings, debriefings and appropriate communication tools, such as e-mail, personal websites, teleconferences, etc.

A characteristic feature of intelligent systems is the presence of knowledge necessary to solve problems in a specific subject area.

Introduction to Knowledge Engineering

2. Knowledge as a special form of information. The difference between knowledge and data.

5. Knowledge division. Declarative and procedural forms of knowledge representation

6. Properties of knowledge

7. Intension and extension of the concept.

8. Knowledge representation paradigms. Classification of knowledge representation models

Theoretical and practical issues representation and processing of knowledge in computer systems Researchers working in the field of knowledge engineering are actively involved. This concept was introduced in 1977 by E. Feigenbaum, who wrote: “We know from experience that most knowledge in a particular subject area remains the personal property of the expert. And this happens not because he does not want to divulge his secrets, but because he is unable to do this - after all, the expert knows much more than he himself realizes.” Knowledge engineering is a branch of AI that is associated with the development of theoretical and applied aspects of the acquisition and formalization of specialist knowledge, with the design and development of knowledge bases.

Datacall information of a factual nature that describes objects, processes and phenomena of the subject area, as well as their properties. In computer processing processes, data undergoes the following stages of transformation:

the initial form of data existence (results of observations and measurements, tables, reference books, diagrams, graphs, etc.);

presentation on special languages descriptions of data intended for input and processing of source data into a computer;

databases on computer storage media.

Knowledgeare a more complex category of information compared to data. Knowledge describes not only individual facts, but also the relationships between them, which is why knowledge is sometimes called structured data. Knowledge can be obtained based on the processing of empirical data. They are the result of a person’s mental activity aimed at generalizing his experience gained as a result of practical activity.

3. Methods of imparting knowledge software systems

In order to provide IIS with knowledge, it must be presented in a certain form. There are two main ways to impart knowledge to software systems.

The first is to put knowledge into a program written in a regular programming language. Such a system will represent a single program code, in which knowledge is not placed in a separate category. Despite the fact that the main problem will be solved, in this case it is difficult to assess the role of knowledge and understand how it is used in the process of solving problems. Modification and maintenance are not easy similar programs, and the problem of replenishing knowledge may become insoluble.

The second method is based on the concept of databases and consists of placing knowledge in a separate category, i.e. knowledge is presented in a specific format and placed in the knowledge base. The knowledge base is easily updated and modified. It is an autonomous part of an intelligent system, although the logical inference mechanism implemented in the logical block, as well as the means of dialogue, impose certain restrictions on the structure of the knowledge base and operations with it. This method is adopted in modern IIS.

The task of representing knowledge in information systems

In order to put knowledge into a computer, it must be represented in certain ways. data structures, corresponding to the selected intelligent system development environment. Consequently, when developing an information information system, knowledge is first accumulated and presented, and at this stage human participation is required, and then the knowledge is represented by certain data structures that are convenient for storage and processing in a computer.

Knowledge in MIS exists in following forms:

initial knowledge (rules derived from practical experience, mathematical and empirical dependencies reflecting mutual connections between facts; patterns and trends that describe how facts change over time; functions, diagrams, graphs, etc.);

description of initial knowledge using the selected knowledge representation model (set of logical formulas or production rules, semantic web, frame hierarchy, etc.);

representation of knowledge by data structures that are intended for storage and processing on a computer;

knowledge bases on computer storage media.

4. Definition of knowledge

From explanatory dictionary S.I. Ozhegova: 1) “Knowledge - comprehension of reality by consciousness, science”; 2) “Knowledge is the totality of information, knowledge in any area.”

From the Japanese explanatory dictionary: “Knowledge is the result obtained by cognition,” or, in more detail, “a system of judgments with a principled and unified organization based on an objective law.”

AI researchers provide more specific definitions of knowledge.

“Knowledge is the laws of a subject area (principles, connections, laws) obtained as a result of practical activities and professional experience, allowing specialists to set and solve problems in this area

“Knowledge is well-structured data, or data about data, or metadata”

“Knowledge is formalized information that is referred to or used in the process of logical inference”

Domain knowledge - this is a description of objects, their environment, necessary phenomena, facts, as well as the relationships between them.

5. Knowledge Division

There are many classifications of knowledge. At the same time, with the help of classifications, as a rule, knowledge of specific subject areas is systematized. At an abstract level of consideration, we can talk about the characteristics by which knowledge is divided, and not about classifications.

By its nature, knowledge can be divided into declarative and procedural.

Declarativee knowledge is a description of facts and phenomena, records the presence or absence of such facts, and also includes descriptions of the basic connections and patterns in which these facts and phenomena are included.

Proceduralknowledge is a description of actions that are possible when manipulating facts and phenomena to achieve intended goals.

By method of acquisition knowledge can be divided into facts and heuristics (rules that allow you to make a choice in the absence of precise theoretical justifications). The first category of knowledge usually indicates well-known circumstances in a given subject area. The second category of knowledge is based on own experience an expert working in a specific subject area, accumulated as a result of many years of practice.

By representation type knowledge is divided into facts and rules. Facts are knowledge of the “A is A” type; such knowledge is typical for databases and network models. Rules, or products, are knowledge of the “IF A, THEN B” type.

Factual and strategic knowledge. Factual knowledge is the basic laws of a subject area that allow solving specific production, scientific and other problems, that is, facts, concepts, relationships, assessments, rules, heuristics. Strategic knowledge - decision-making strategies in the subject area;

In addition to facts and rules, there are also metaknowledge- knowledge about knowledge. They are necessary for knowledge management and for effective organization inference procedures. Classic examples of metaknowledge are folk proverbs and sayings, each of which characterizes knowledge (recommendations for activities) in a wide class specific situations(for example, the proverb “Measure seven times, cut once” is applicable not only among surgeons or tailors. “If you don’t know the ford, don’t stick your nose into the water”).

Deep and superficial knowledge. In-depth knowledge reflects an understanding of the structure of the subject area, the purpose and relationship of individual concepts (in-depth knowledge in the fundamental sciences is laws and theoretical foundations). Deep knowledge is formed as a result of the generalization of primary concepts into some abstract structures that may not have a verbal description. Deep knowledge has such important features, as flexibility and additivity (lat. additio - addition; obtained by addition. Superficial knowledge is a set of empirical associations and relationships between concepts of the subject area for standard reasoning and situations

Hard and soft knowledge. Hard knowledge allows you to obtain unambiguous recommendations under given initial conditions. Soft knowledge allows for multiple, “fuzzy” solutions and various options recommendations.

In the practice of developing AIS, there has been a tendency to move from the use of superficial knowledge to deep and soft knowledge. The use of deep and soft knowledge allows you to create a knowledge base of great power.).

Everyone knows what databases are and how to use them. A wide variety of databases have been created and are constantly expanding on any topic, from scientific periodicals to fiction, from works of art to telephone directories.

But this necessary education is gradually beginning to lose its former significance. This is especially true for scientific periodicals. Main problem scientific databases is their redundancy. Any request made using keywords search words, will give this huge amount links that viewing them becomes separate work. However, many materials differ so slightly that it is difficult to assess the usefulness of one of them against the background of another.

A way out of this situation is to create knowledge bases or decision bases: systematized information that is processed using other search algorithms.

What is the main difference between databases and knowledge bases? The database is searched by keyword, relatively speaking, this is the answer to the question “what?”. For example, we ask search query“nanotubes”. The database will return everything related to this request: synthesis, oxidation, biodegradation, and spectral characteristics. The number of links will exceed thousands. You can search by two, three or more keywords. This will reduce the flow of links, but may cut off the necessary ones. In the knowledge base, the search is carried out using several questions, for example: “What?”, “With what?”, “How?”. This brings up the next point. Currently, millions of articles and patents have been written in all areas of knowledge. But there are only about 30 - 35 thousand solutions that meet the knowledge base principle. The increase in the number of decisions, in contrast to the increase in the number of articles, is slow. The vast majority of articles are only small nuances of a solution. For example: metal hardening. Solution - what: metal, what: cooling material, how: quickly. This solution covers all metals and alloys, all types of quenching liquids or gases, and all methods of refrigerant supply. Further, from this request, a database can be formed, for example, by types of refrigerant (water, oil, brines), the second - by methods of supplying the material (pumps, dipping a part, spraying a solution), the third - by steel grades. An additional database of links can be formed on minor processes: oxidation of the metal surface, removal of carbon deposits after hardening, special methods hardening Searching a knowledge base is different from searching a database; it uses so-called “resources”. Resources in understanding knowledge bases are materials, catalysts, fields and influences that lead to a solution. Knowledge bases can also process search questions. For example, the query “synthesize ester” entered into the database will be interpreted only by the keyword “ester”. In the knowledge base, you can also specify the terms “synthesis”, “decay”, “biodegradation” and semantic search algorithms for verbs.

Now a little about the disadvantages of this system. Databases are established rules of formation keywords, uniform (with minor variations) for all scientific publications and unified with search algorithms. Knowledge bases will need to be created from scratch. This is a lot of work, because in order to isolate resources, you need a complete understanding of the processes described in the article or patent, which becomes much more difficult when processing multidisciplinary articles and patents protected from reengineering. The second disadvantage is that knowledge bases are now being created “for engineers,” that is, mainly with an applied focus. Fundamental research, therefore, does not fall into them.

Now a little about the advantages. Building a knowledge base is a great learning process. A “by-product” is a significant increase in the level of knowledge of developers and the receipt of highly qualified specialists who can decide assigned tasks. The second plus is that with a certain algorithm for generating queries, the knowledge base can be a source of new solutions that have not been described and have not yet been created. For example, when asking for metal hardening, the knowledge base can produce a list of resources that have the necessary properties (temperature, fluidity) and prompt the creation of new solutions, such as hardening in polymer melts, hardening with simultaneous oxidation of the surface, spot and uneven hardening. Third plus. Probably, many did not even think that the essence of the processes outlined in a scientific article or patent is formulated in no more than a hundred words. At the same time, the volume of articles is at least several pages, and patents - up to several hundred pages. Processing the material into a knowledge base system will make it possible in the future not to waste time reading insignificant details and differences from analogues, which are certainly described in the source materials.

A short summary. Knowledge bases are extremely useful for applied developments, especially at the cutting edge of science. They allow you to receive ready-made solutions for one task or another. At the same time, their creation greatly increases the professional level of developers and allows them to obtain excellent specialists.