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We propose a new approach to the use of circumscription for representing knowledge.
This chapter describes a nonmonotonic causal logic designed for representing knowledge about the effects of actions.
In this approach, the concept of utility, describing the usefulness of a specific configuration, is utilized for representing knowledge.
We also outline the main methods used by these algorithms for representing knowledge about shape, identity, and relationships.
Modal logics are currently widely accepted as a suitable tool of knowledge representation, and the question what logics are better suited for representing knowledge is of particular importance.
RDF technologies provide a domain-independent way for representing knowledge and their open nature assures many alternative approaches for making data available as RDF.
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A model, as the term is used here, is a way of representing knowledge for the purpose of thinking, communicating to others, or implementing decisions as in system analysis, design or operations.
Ontologies are becoming increasingly commonplace for semantically representing knowledge in a formal manner that facilitates sharing of rich information between people and applications.
We conclude this section on domain models by stating our next principle: Partial Order Knowledge Structures (POKS) or Directed Acyclic Graphs (DAG) are optimal for formally representing knowledge domains.
This technology is currently being used for representing human knowledge and as critical components in knowledge management, semantic web, business to business applications, bioinformatics, e-learning, etc.
Once the underlying medical knowledge on the appropriate use of genetic and genomic interventions is available, various formalisms are available for representing that knowledge in an appropriate computer-processable format [ 49, 50].
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