Knowledge Representation

Research output: Chapter in Book/Report/Conference proceedingChapter


In artificial intelligence, knowledge representation is the study of how the beliefs, intentions, and value judgments of an intelligent agent can be expressed in a transparent, symbolic notation suitable for automated reasoning. From a purely computational point of view, the major objectives to be achieved are breadth of scope, expressivity, precision, support of efficient inference, learnability, robustness, and ease of construction. Knowledge-based techniques have been applied successfully for many computational tasks including text interpretation and cognitive robotics. Many different general architectures have been used for knowledge representation, including first-order logic, other formal logics, semantic networks, and frame-based systems. The representation of temporal knowledge is both a problem of central importance in knowledge representation and an archetype of the kinds of issues that arise in developing representations for various domains. 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.

Original languageEnglish (US)
Title of host publicationInternational Encyclopedia of the Social & Behavioral Sciences: Second Edition
PublisherElsevier Inc.
Number of pages7
ISBN (Electronic)9780080970875
ISBN (Print)9780080970868
StatePublished - Mar 26 2015


  • Artificial intelligence
  • First-order logic
  • Frame
  • Knowledge base
  • Knowledge representation
  • Logic
  • Reasoning
  • Representation
  • Semantic network
  • Temporal logic

ASJC Scopus subject areas

  • General Social Sciences


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