Information and data are their difference from information. Concepts of information given by various sciences
When thinking about the difference between information and data, you can’t help but wonder if they have anything in common?
We so often replace one word with another in speech that we do not notice how our statements become absurd. In order not to get into a stupid situation, you should figure out what each of them means.
There is a gap between data and information close connection that the existence of one without the other is either impossible or simply meaningless.
Data is the basis of information. Essentially, they are just a set of characters. But after they have been interpreted by some perceiving system, the data becomes information.
Occurrence condition
So, information arises only if there is a certain source containing data and, directly, a recipient. Data can be transformed into information in several ways: through counting, correction, compression, contextualization and categorization.
Data is information recorded on some source. IN Lately The amount of data has grown incredibly. This was caused by the rapid growth of the Internet.
Measurement
Data cannot be measured. As soon as we begin to count the data, the processing process will begin. This means that the data will automatically move into the category of “information”. Information can be measured. To do this, it is enough to assess the level of knowledge before and after the receipt of information.
Conversion Result
The human brain, like the most advanced computer, processes the data we receive and produces some information. And when the need arises to apply it to another thought process, then for him this information in turn becomes data from which new information will be obtained.
The final stage of transformation of information that has undergone repeated processing over a certain period of time becomes knowledge.
Conclusions website
- Data and information are closely interconnected.
- Data is fixed; it actually exists in each unit of time. Information arises only when this data is processed.
- Data after transformation becomes information. Repeatedly verified information - knowledge.
- Information, unlike data, is a measurable substance.
5.1. Differences between knowledge and data
A characteristic feature of intelligent systems is the presence of knowledge necessary to solve problems of a specific subject area. This raises a natural question: what is knowledge and how does it differ from ordinary data processed by a computer?
Data is information of a factual nature that describes objects, processes and phenomena of the subject area, as well as their properties. In processes computer processing The data goes through the following stages of transformation:
The initial form of existence of data (results of observations and measurements, tables, reference books, diagrams, graphs, etc.);
Presentation in special languages of description of data intended for input and processing of initial data into a computer;
Databases on computer storage media.
Knowledge is 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.
In order to provide IIS with knowledge, it must be presented in a certain form. There are two main ways of imparting knowledge 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 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.
It should be noted that in order to put knowledge into a computer, it must be represented by certain data structures corresponding to the chosen environment for developing an intelligent system. 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 IIS exists in the following forms:
Initial knowledge (rules derived from practical experience, mathematical and empirical dependencies reflecting mutual connections between facts; patterns and trends describing changes in facts over time; functions, diagrams, graphs, etc.);
Description of initial knowledge by means of the selected knowledge representation model (many logical formulas or production rules, semantic network, frames, etc.);
Representation of knowledge by data structures that are intended for storage and processing on a computer;
Knowledge bases on computer storage media.
What is knowledge? Let's give a few definitions.
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.”
The definition of the term “knowledge” includes mostly philosophical elements. For example, knowledge is a practice-tested result of cognition of reality, its correct reflection in the human mind.
Knowledge is the result obtained by understanding the surrounding world and its objects. In the simplest situations, knowledge is considered as a statement of facts and their description.
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.”
In the field of AI systems and knowledge engineering, the definition of knowledge is linked to logical inference: knowledge is information on the basis of which the process of logical inference is implemented, i.e. Based on this information, various conclusions can be drawn from the data available in the system using logical inference. The inference mechanism allows you to link individual fragments together, and then draw a conclusion based on this sequence of related fragments.
Knowledge is formalized information that is referred to or used in the process of logical inference (Fig. 5.1.).
Rice. 5.1. Inference process in IS
By knowledge we mean a set of facts and rules. The concept of a rule representing a piece of knowledge has the form:
If<условие>That<действие>.
This definition is special case previous definition.
However, it is recognized that the distinctive qualitative features of knowledge are due to the presence of great opportunities in the direction of structuring and interconnectedness of constituent units, their interpretability, the presence of metrics, functional integrity, and activity.
There are many classifications of knowledge. As a rule, with the help of classifications, 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.
Declarative 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.
Procedural knowledge is a description of actions that are possible when manipulating facts and phenomena to achieve intended goals.
To describe knowledge at an abstract level, special languages have been developed - knowledge description languages. These languages are also divided into procedural and declarative languages. All knowledge description languages oriented toward the use of traditional von Neumann architecture computers are procedural languages. The development of declarative languages that are convenient for representing knowledge is a pressing problem today.
According to the method of acquiring knowledge, it can be divided into facts and heuristics (rules that allow you to make a choice in the absence of precise theoretical justification). The first category of knowledge usually indicates well-known circumstances in a given subject area. The second category of knowledge is based on the own experience of an expert working in a specific subject area, accumulated as a result of many years of practice.
Based on the type of representation, 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.
In addition to facts and rules, there is also metaknowledge - knowledge about knowledge. They are necessary for knowledge management and for the effective organization of logical inference procedures.
The form of knowledge representation has a significant impact on the characteristics of information information systems. Knowledge bases are models of human knowledge. However, all the knowledge that a person uses in the process of solving complex problems cannot be modeled. Therefore, in intelligent systems it is necessary to clearly separate knowledge into those that are intended to be processed by a computer and knowledge used by humans. Obviously, in order to solve complex problems, the knowledge base must have a sufficiently large volume, and therefore problems of managing such a database inevitably arise. Therefore, when choosing a knowledge representation model, factors such as uniformity of representation and ease of understanding should be taken into account. The homogeneity of the presentation leads to a simplification of the knowledge management mechanism. Ease of understanding is important for users of intelligent systems and experts whose knowledge is embedded in the information information system. If the form of knowledge representation is difficult to understand, then the processes of acquiring and interpreting knowledge become more complicated. It should be noted that simultaneously meeting these requirements is quite difficult, especially in large systems where structuring and modular representation of knowledge becomes inevitable.
Solving knowledge engineering problems poses the problem of converting information received from experts in the form of facts and rules for their use into a form that can be effectively implemented through machine processing of this information. For this purpose, various knowledge representation models have been created and used in existing systems.
TO classic models knowledge representations include logical, production, frame and semantic network models.
Each model has its own knowledge representation language. However, in practice, it is rarely possible to manage within the framework of one model when developing an information information system, except for the simplest cases, so the representation of knowledge turns out to be complex. In addition to the combined representation using various models, commonly used special means, allowing to reflect the features of specific knowledge about the subject area, as well as various ways eliminating and taking into account vagueness and incompleteness of knowledge.
Concept, structure, classification, features of intelligent systems.
A system is called intelligent if it implements 3 basic functions:
1. Representation and processing of knowledge.
2. Reasoning.
3. Communication.
User
Functional Mechanisms Knowledge Base
Structural knowledge – knowledge about operating environment. Metaknowledge is knowledge about the properties of knowledge.
1. Biochemical (everything related to the brain);
2. Software-pragmatic direction (writing programs that replace functions).
1. Local (task) approach: for each task special programs who achieve results no worse than humans.
2. Systematic approach based on knowledge – creation of automation tools, creation of the programs themselves.
3. An approach using the method of procedural programming - creating algorithms in natural languages.
Main sections of IIT:
1. Knowledge management.
2. Formal languages and semantics.
3. Quantum semantics.
4. Cognitive modeling.
5. Convergent (converging) decision support systems.
6. Evolutionary genetic algorithms.
7. Neural networks.
8. Ant and immune algorithms.
9. Expert systems.
10. Fuzzy sets and calculations.
11. Nonmonotonic logics.
12. Active multi-agent systems.
13. Natural language communication and translation.
14. Pattern recognition, playing chess.
Characteristics of problem areas where the use of information information systems is necessary:
1. Quality and efficiency of decision making.
2. Unclear goals.
3. Chaotic, fluctuating and quantized behavior of the environment.
4. Multiplicity of factors that replace each other.
5. Weak formalizability.
6. Uniqueness (non-stereotypicality) of the situation.
7. Latency (hiddenness) of information.
8. Deviance in the implementation of plans, as well as the significance of small actions.
9. Paradoxical logic of decisions.
Instability, lack of focus, chaotic environment
Concept of data, information and knowledge. Properties of knowledge and their difference from data.
Information is:
· any information received and transmitted, stored by various sources;
· this is the entire set of information about the world around us, about all kinds of processes occurring in it that can be perceived by living organisms, electronic machines and other information systems;
· this is significant information about something, when the form of its presentation is also information, that is, it has a formatting function in accordance with its own nature;
· this is all that can be added to our knowledge and assumptions.
Data is 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:
· original form existence of data (results of observations and measurements, tables, reference books, charts, graphs, etc.);
· presentation in special languages of descriptions of data intended for input and processing of source data into a computer;
· databases on computer storage media.
Knowledge - in theory artificial intelligence and expert systems - a set of information and rules of inference (from an individual, society or an AI system) about the world, the properties of objects, the patterns of processes and phenomena, as well as the rules for using them for decision making. The main difference between knowledge and data is their structure and activity; the appearance of new facts in the database or the establishment of new connections can become a source of changes in decision making.
In order to place knowledge into an information system, it must be represented by certain data structures that correspond to the chosen environment for developing an intelligent system. Therefore, when developing an 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.
IP knowledge exists in the following forms:
· initial knowledge (rules derived from practical experience, mathematical and empirical dependencies reflecting mutual connections between facts; patterns and trends describing changes in facts over time; functions, diagrams, graphs, etc.);
· description of initial knowledge by means of the selected knowledge representation model (set of logical formulas or production rules, semantic network, hierarchies of frames, etc.);
· representation of knowledge by data structures that are intended for storage and processing on a computer;
· knowledge bases on computer storage media.
Knowledge is 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.
Properties of knowledge (from lectures):
· Internal interpretability (data + method data). Methodological - structured data, which represents the characteristics of the described entities for the purposes of their identification, search, evaluation, and management
· Availability of connections (internal, external), communication structure
· Possibility of scaling (assessment of the relationship between information units) – quantitative
· Availability of semantic metrics (a means of assessing poorly formalized information units)
· The presence of activity (incompleteness, inaccuracy encourages them to develop, replenish).
Classification of knowledge
Knowledge– a form of existence and systematization of the results of human cognitive activity. Knowledge helps people rationally organize their activities and decide various problems, arising in its process.
Knowledge(in the theory of artificial intelligence and expert systems) - a set of information and rules of inference (from an individual, society or an AI system) about the world, the properties of objects, the patterns of processes and phenomena, as well as the rules for using them for decision making.
The main difference between knowledge and data is their structure and activity; the appearance of new facts in the database or the establishment of new connections can become a source of changes in decision making.
Highlight different kinds knowledge:
Scientific,
Extra-scientific,
Ordinary-practical (ordinary, common sense),
Intuitive,
Religious, etc.
Everyday practical knowledge is unsystematic, unsubstantiated, and unwritten. Ordinary knowledge serves as the basis for a person’s orientation in the world around him, the basis for his everyday behavior and foresight, but usually contains errors and contradictions. Scientific knowledge based on rationality is characterized by objectivity and universality, and claims to be universally valid. Its task is to describe, explain and predict the process and phenomenon of reality. Extrascientific knowledge is produced by a certain intellectual community according to norms and standards that differ from rationalistic ones; they have their own sources and means of knowledge.
Classification of knowledge
I. by nature. Knowledge can be declarative And procedural.
Declarative knowledge contain only an idea of the structure of certain concepts. This knowledge is close to data and facts. For example: a higher educational institution is a collection of faculties, and each faculty, in turn, is a collection of departments. Procedural knowledge is of an active nature. They define ideas about the means and ways of obtaining new knowledge and testing knowledge. These are different types of algorithms. For example: method brainstorming to search for new ideas.
II. according to the degree of science. Knowledge can be scientific And extra-scientific.Scientific knowledge can be:
1) empirical (based on experience or observation);
2) theoretical (based on the analysis of abstract models, analogies, diagrams that reflect the structure and nature of processes, i.e. generalization of empirical data).
Extra-scientific knowledge can be:
parascientific knowledge - teachings or reflections about phenomena, the explanation of which is not convincing from the point of view of scientific criteria.
pseudoscientific – deliberately exploiting conjectures and prejudices.
quasi-scientific - they are looking for supporters and adherents, relying on methods of violence and coercion. Quasi-scientific knowledge, as a rule, flourishes in conditions of strictly hierarchical science, where criticism of those in power is impossible, where the ideological regime is strictly manifested. (In the history of Russia, the periods of “triumph of quasi-science” are well known: Lysenkoism; fixism, etc.)
anti-scientific - as utopian and deliberately distorting ideas about reality.
pseudoscientific - represent intellectual activity that speculates on a set of popular theories (stories about ancient astronauts, about Bigfoot, about the monster from Loch Ness)
everyday-practical - delivering basic information about nature and the surrounding reality. Ordinary knowledge includes common sense, signs, edifications, recipes, and personal experience, and traditions. Although it records the truth, it does so unsystematically and without evidence.
personal – depending on the abilities of a particular subject and on the characteristics of his intellectual cognitive activity. Collective knowledge is generally valid (transpersonal), presupposes the presence of concepts, methods, techniques and rules of construction common to the entire system. III. by location
Highlight personal(tacit, hidden, not yet formalized) knowledge and formalized(explicit) knowledge.
Tacit knowledge– knowledge of people that has not yet been formalized and cannot be transferred to other people.
Formalized in some language (explicit) knowledge:
knowledge in documents;
knowledge on CDs;
knowledge in personal computers;
Internet knowledge;
knowledge in knowledge bases;
knowledge in expert systems, extracted from the tacit knowledge of human experts.
The distinctive characteristics of knowledge are still a matter of uncertainty in philosophy. According to most thinkers, for something to be considered knowledge, it must satisfy three criteria:
a) be confirmed,
b) be true,
c) trustworthy.
Related information.
Data- this is also knowledge, but knowledge of a very special kind. To a first approximation, data is the result of linguistic recording of a single observation, experiment, fact or situation. Examples of data could be:
a) “on such and such a date, such and such a year, at moment t it was raining in a certain area” (meteorological data)”;
b) “the price of commercial timber on such and such a day of such and such a year, according to information from such and such an exchange, was so many dollars per ton” (trade data);
c) “the state budget deficit in such and such a country amounted to so and so billions of dollars in such and such a year” (financial datum);
d) “at such and such a moment in time, the automatic laboratory heading towards Jupiter deviated from the calculated trajectory by so many degrees, so many thousands of kilometers in such and such a direction” (data from the field of space technology).
From a technological point of view, some experts usually define the concept of “data” as information that is stored in databases and processed application programs, or information presented as a sequence of characters and intended for processing in a computer, i.e. data includes only that part of knowledge that is formalized to such an extent that formalized processing procedures can be carried out on it using various technical means.
Data is information presented in a formalized form suitable for automatic processing with possible human participation. Data is information written (encoded) in the language of the machine. Data are individual facts that characterize objects, processes and phenomena in the subject area, as well as their properties.
There is a difference between information and data; Data can be considered as signs or recorded observations that for some reason are not used, but only stored. Therefore, in this moment Over time, they do not influence behavior or decision-making. However, data turns into information if such an impact exists.
For example, the main body of data for a computer consists of features that do not affect behavior. Unless this data is organized appropriately and reflected in the form of an output so that the manager acts in accordance with it, it is not information. They remain data until the employee accesses them in connection with the implementation of certain actions or in connection with some decision that he is obliged to make.
Data turns into information when its meaning is realized. It can also be said that when it is possible to use data to reduce uncertainty about something, data turns into information.
Data Life Cycles
Like matter and energy, data can be collected, processed, stored, and changed in form. However, they have some features. First of all, data can be created and disappeared. For example, data on an extinct animal may disappear when a piece of coal with its prints is burned. Data may be erased, lose accuracy, etc. Data can be characterized by a life cycle (Fig. 1.9), in which three aspects are of primary importance - generation, processing, storage and retrieval.
Reproduction and use of data can occur at different points in their life cycle and are therefore not shown in the diagram.
Rice. 1.9. Life cycle of data
When processed on a computer, data is transformed, conditionally going through the following stages:
1) data as a result of measurements and observations:
2) data on material media information (tables, protocols, reference books);
3) data models (structures) in the form of diagrams, graphs, functions;
4) data in the computer in a data description language;
5) databases on computer media.
Data Models
The data model is the core of any database. The appearance of this term in the early 70s of the twentieth century is associated with the works of the American cybernetics E.F. Codd, which reflected the mathematical aspect of the data model used in the sense of data structure. In connection with the needs for the development of data processing technology in the theory of automated information banks (ABI), in the second half of the 70s, the instrumental aspect of the data model appeared; the content of this term included restrictions imposed on data structures and operations with them.
In a modern interpretation data model is defined as a set of rules for generating data structures in databases, operations on them, as well as integrity constraints that determine permissible connections and data values, and the sequence of their changes.
Thus, a data model represents a set of data structures, integrity constraints, and data manipulation operations. Based on this, we can formulate the following working definition: A data model is a set of data structures and processing operations.
Currently, there are three main types of data models: hierarchical, network and relational. Hierarchical data model organizes data in the form of a tree structure and is the implementation of logical connections: generic relations or “whole - part” relations. For example, the structure of higher education educational institution is a multi-level hierarchy (see Fig. 1.10).
Rice. 1.10. Example of a hierarchical structure
A hierarchical (tree) database consists of an ordered set of trees; more precisely, from an ordered set of multiple instances of the same type of tree. In this model source elements generate other elements, and these elements in turn generate the following elements. Each child element has only one parent element. Organizational structures, lists of materials, tables of contents in books, project plans, meeting schedules, and many other sets of data can be presented in a hierarchical form.
The main disadvantages of this model are: a) the complexity of displaying the relationship between objects of the “many to many” type; b) the need to use the hierarchy that was the basis of the database during design. The need for constant reorganization of data (and often the impossibility of this reorganization) has led to the creation of more general model– network.
The network approach to data organization is an extension of the hierarchical approach. This model differs from hierarchical in that each child element can have more than one parent element. An example of a network data model is shown in Figure 1.11.
Because a network database can directly represent all kinds of relationships inherent in the data of the relevant organization, this data can be navigated, explored and queried in every possible way, i.e. network model not bound by just one hierarchy. However, in order to make a request to a network database, you need to delve deeply into its structure (have the schema of this database at hand) and develop your own mechanism for navigating the database, which is a significant drawback of this database model.
Rice. 1.11. Example network structure
One of the disadvantages of the data models discussed above is that in some cases, with a hierarchical and network representation, the growth of the database can lead to a violation of the logical representation of the data. Such situations arise when new users, new applications and types of requests appear, taking into account other logical connections between data elements. The relational data model avoids these disadvantages.
A relational database is one in which all data is presented to the user in the form of rectangular tables of data values, and all operations on the database are reduced to manipulations with tables.
A table consists of columns (fields) and rows (records); has a name that is unique within the database. The table reflects the type of real world object (entity), and each of its rows represents a specific object. Thus, the Sports section table contains information about all children involved in a given sports section, and its rows represent a set of attribute values for each specific child. Each table column is a collection of values for a specific attribute of an object. The Weight column, for example, represents the totality of all weight categories of children involved in the section. The Gender column can only contain two different meanings: "husband." and "feminine." These values are selected from the set of all possible values for an object's attribute, called the domain. Thus, the values in the Weight column are selected from the set of all possible child weights.
Each column has a name, which is usually written at the top of the table. These columns are called fields tables. When designing tables within a specific DBMS, it is possible to select for each field its type, those. define a set of rules for its display, as well as determine the operations that can be performed on the data stored in this field. Sets of types may vary between different DBMSs.
The field name must be unique in the table, but different tables can have fields with the same name. Any table must have at least, one field; The fields are located in the table in accordance with the order in which their names appeared when it was created. Unlike fields, strings do not have names; their order in the table is not defined, and their number is logically unlimited. The lines are called records tables.
Since the rows in the table are not ordered, it is impossible to select a row by its position - there is no "first", "second", or "last" among them. Any table has one or more columns, the values of which uniquely identify each of its rows. This column (or combination of columns) is called a primary key. In the Sports section table, the primary key is the Full Name column. (Fig. 1.12).
This choice primary key It has significant drawback: it is impossible to register two children in a section with the same value in the Full Name field, which in practice is not so rare. That is why an artificial field is often introduced to number records in the table. Such a field, for example, could be a journal number for each child, which can ensure the uniqueness of each entry in the table. If a table satisfies this requirement, it is called attitude(relation).
Rice. 1.12. Relational data model
Relational Models data can usually support four types of relationships between tables:
1) One to one(example: one table stores information about schoolchildren, another table stores information about schoolchildren’s vaccinations).
2) One to Many(example: one table stores information about teachers, another table stores information about students for whom these teachers are class teachers).
3) Many to One(as an example, we can offer the previous case, considering it from the other side, namely from the side of the table in which information about schoolchildren is stored).
4) Many to Many(example: orders for the supply of goods are stored in one table, and in another - companies executing these orders, and several companies can be combined to fulfill one order /
Relational representation of data has a number of advantages. It is understandable to a user who is not a programming specialist, allows you to easily add new descriptions of objects and their characteristics, and has great flexibility when processing queries.
Questions and tasks
1. Define the concept of “data”.
2. What is the data life cycle?
3. What data models do you know?
4. List the advantages and disadvantages of each data model.
INFORMATION PROCESSES
Before continuing to consider the issues of knowledge management, it is important to define the key concepts of this area: “data”, “information”, “knowledge”.
The literature on knowledge management presents different approaches to its interpretation. Without pretending to be a full-scale analysis, we will try to outline some important points.
Under data unordered observations, numbers, words, sounds, images are understood. This is a set of discrete, objective factors about events. Moreover, in an organizational context, data is interpreted as structured records of acts of activity. Organizations typically store data in information systems, to which they come from various departments and services.
When data is organized, ordered, grouped, categorized, it becomes information. It is interpreted as a collection of data arranged for a specific purpose that gives it meaning.
Message- this is text, digital data, images, sound, graphics, tables, etc.
Intelligence– practically synonymous with the concept of “Messages”. They are most often of a domestic nature.
Knowledge it is interpreted as information that is ready for productive use, effective, and equipped with meaning. It is a collection of formalized experiences, values, contextual information, and expert understanding that form the basis for evaluating and integrating new experiences and information. It is formed and applied in the minds of people, and in organizations it is often enshrined not only in documents and repositories, but also in organizational procedures, processes, ways of doing things and norms.
The table provides various definitions of knowledge based on a review of the literature.
Most of the definitions discussed emphasize that knowledge is a broader, deeper and richer concept compared to information. They represent mobile connection different elements– experience, values, information and expert understanding- and constantly changing; they are intuitive; are characteristic of people and are an integral part of the human essence with its unpredictability.