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A wide variety of processes and knowledge representations are used to carry out these steps.
In order to study the decision-making phase of the global diagnosis synopsis suggested in Fig. 1, two different knowledge representations are considered from only one information source, as shown in Fig. 2.
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The use of machine learning techniques for the automatic construction of knowledge bases and knowledge representations is difficult, but has achieved some degree of success.
So, bridging the discrepancy in these two knowledge representations is a challenging problem.
Aspects of a lean and modular knowledge representation are described.
Methods and structures of the proposed architecture, including a hybrid knowledge representation, are presented and discussed.
The main advantages of the semantic networks as formalism for knowledge representation are well known: simplicity, naturalness, visionless, and clarity.
Knowledge engineering and knowledge representation are the scientific areas that describe ways in which human knowledge may be represented in computer systems (the so called knowledge-based systems).
A number of general architectures for knowledge representation are described, including first-order logic, other formal logics, semantic networks, and frame-based systems.
Frames for knowledge representation are advantageous because they can be created, visualized, and conceptualized as self-contained entities that correspond to accepted medical constructs.
Formal methodologies and techniques of knowledge representation are fundamental tools for the governance of land transformations and for sharing information among involved subjects.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com