Knowledge Representation - Rhizomik
Finally, Prolog incorporates some built-in predicates and functions that provide useful primitives and non-logical programming facilities, e.g. computer input/output management. They are the facilities and building blocks over which logic programs and personalised predicates and functions are defined. They conform the ontology that captures the knowledge structures the Prolog knowledge base over which logic programs work.
Human Knowledge: Foundations and Limits
Therefore, Knowledge Representation can be defined as the application of logic and ontology to the task of constructing computable models of some domain . Logic and Ontology provide the formalisation mechanisms required to make expressive models easily sharable and computer aware. Finally, thanks to computational resources, great quantities of knowledge expressed this way can be automated. Thus, the full potential of knowledge accumulations can be exploited. However, computers play only the role of powerful processors of more or less rich information sources. The final interpretation of the results is carried out by the agents that motivate this processing, in this case human users of the knowledge management systems.
For social agents, tacit knowledge is also stored distributed in common habits established in a community [,]. The same principles apply, although from the perspective of the whole community as an agent. It can be also considered tacit because it is not explicitly represented in the community. It is distributed while agent act collectively, for example by imitation. This process is also known as socialisation , a complete view of the tacit-explicit knowledge cycle is shown in . Human natural languages are an example of tacit shared knowledge. Although a part of natural languages can be formalised, humans acquire natural language abilities mainly by imitation.
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Meanwhile, philosophy studies the nature of knowledge, how we create and manage it. Some techniques have been developed that capture a part of our brain operation. Most of them use mathematical tools to some extent. For instance, logic and ontology are two building blocks of Knowledge Representation. On the other hand, there are also attempts to explain mathematics from a philosophical point of view .
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This is one of the fundamental aspects of knowledge representation as presented in the . Logic was developed as an attempt to create a universal language based on mathematical principles. Therefore, it is based on formal principles that impose some requirements over a knowledge representation language to be a logic:
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Natural Languages can represent a wider range of knowledge, however, logic enables the precisely formulated subset to be expressed in computable form. On the other hand, although there are some kinds of knowledge not expressible in logic, such knowledge cannot be represented either on any digital computer in any other notation. The expressible power of logic includes every kind of information storable or programmed on any digital computer.
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Semiotics studies signs, that comprise icons, tokens, symbols, etc., and thus the complete representations range. It covers them in general: their use in language and reasoning and their relationships to the world, to the agents who use them, and to each other. Therefore, it comprises all languages, informal and formal, presented in previous sections.
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It must be remarked that common knowledge representation systems work at the representational level. They manage pieces of information and relate them to senses previously established by knowledge representation means. These sense definitions are mainly captured by ontologies, one of the components of knowledge representation. The other non-computational component, logic, may also capture some representational semantics as built-in ontologies.