Peculiarities of NodeMind

2. Knowledge Representation
Peculiarities of NodeMind:
— fast creating and modifying of schemes;
— no tensive menu;
— infinite map field;
— storage of high-volumed information in the different map objects;
— attachment of files and hyperlinks to each object;
— making screenshots of a map as .jpg files;
— printing of map parts;
— comfortable export/import of data to be interchanged among people;
— easy-to-use events-reminding system for each object (i.e. daily, weekly, monthly, quarterly and annually).
NodeMind provides possibility to create huge but easy-to-work schemes and ensures safety of all data with respect to possibility of creating as many map copies as possible.
Why NodeMind maps are better than paper?
— copying of paper data carriers requires a lot of resources;
— amending of paper schemes is rather complicated process and sometimes even impossible;
— searching process in paper schemes is uncomfortable for a user.
NodeMind is a perfect platform for creating semantic networks.
A semantic network is a network which represents semantic relations among concepts. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges.
Representation of knowledge in the form of semantic network allows you to memorize forgotten knowledge easily, add new information to the existing one and amend with new network objects according to the available network structure. Knowledge presented in the form of semantic network can be easily used as necessary that is essential for planning and analyzing of task solving options.
In NodeMind you are able to create both linked and non-linked structures.
Please see below the example of scheme created in NodeMind:

Moreover, you are able to add additional information either in object or link properties:

Images can be attached as follows:

A user shall possess specific knowledge about semantic network in order to create a map of knowledge. Of course, you may use NodeMind for creating maps and storing information not going deep into the details about semantic network, however, the efficiency will be far less.
Please see below the background data on semantic network.
A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics.
What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge.
Following are six of the most common kinds of semantic networks.
1. Definitional networks emphasize the subtype or is-a relation between a concept type and a newly defined subtype. The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes. Since definitions are true by definition, the information in these networks is often assumed to be necessarily true.
2. Assertional networks are designed to assert propositions. Unlike definitional networks, the information in an assertional network is assumed to be contingently true, unless it is explicitly marked with a modal operator. Some assertional netwoks have been proposed as models of the conceptual structures underlying natural language semantics.
3. Implicational networks use implication as the primary relation for connecting nodes. They may be used to represent patterns of beliefs, causality, or inferences.
4. Executable networks include some mechanism, such as marker passing or attached procedures, which can perform inferences, pass messages, or search for patterns and associations.
5. Learning networks build or extend their representations by acquiring knowledge from examples. The new knowledge may change the old network by adding and deleting nodes and arcs or by modifying numerical values, called weights, associated with the nodes and arcs.
6. Hybrid networks combine two or more of the previous techniques, either in a single network or in separate, but closely interacting networks.
An example of a semantic network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets.
Semantic relations are relations between concepts or meanings. Some of the most common semantic relations defined are:
Active relation: a semantic relation between two concepts, one of which expresses the performance of an operation or process affecting the other.
Antonymy: A is the opposite of B. Example: cold is the opposite of warm.
Associative relation: A is mentally associated with B by somebody. Often unspecified relations are called associative relations.
Causal relation: A is the cause of B. Example: Scurvy is caused by lack of vitamin C.
Homonym: two concepts, A and B, are expressed by the same symbol. Example: Both a financial institution and a edge of a river are expressed by the word ‘bank’.
Hyponymous relationships ("is a" relation or hyponym-hyperonym, genus-species relation): a hierarchical subordinate relation. (A is kind of B; A is subordinate to B; A is narrower than B; B is broader than A). The "is a" relation denotes what class an object is a member of. Example: car — is a — vehicle.
Instance-of relation: (example relation) relations between a general concept and individual instances of that concept. Example: London is an instance of the general concept ‘capital’.
Locative relation: a semantic relation in which a concept indicates a location of a thing designated by another concept. A is located in B.
Meronymy, partitive relation (part-whole relation): a relationship between the whole and its parts (A is part of B). A meronym is the name of a constituent part of, the substance of, or a member of something. Meronymy is opposite to holonymy (B has A as part of itself). (A is narrower than B; B is broader than A).
Passive relation: a semantic relation between two concepts, one of which is affected by or subjected to an operation or process expressed by the other.
Paradigmatic relation: a semantic relation between two concepts that is considered to be either fixed by nature, self-evident, or established by convention. Examples: mother / child; fat /obesity; a state /its capital city.
Polysemy: a polysemous (or polysemantic) word is a word that has several sub-senses which are related with one another. (A1, A2 and A3 shares the same expression)
Possessive: a relation between a possessor and what is possessed.
Synonymy: A denotes the same as B; A is equivalent with B.
Temporal relation: a concept indicates a time or period of an event designated by another concept. Example: Second World War, 1939-1945.
Troponymy: the semantic relation of being a manner of doing something.

Do not forget to encrypt your data by password when creating any map in NodeMind.
You are able to show your maps to your friends that do not have registered version of the program. Registration is not required for reviewing maps created in NodeMind. Registration is required for creation the maps.

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