Knowledge Representation and Reasoning

Concept-based versus linguistic representation

AI knowledge representation systems are usually concept-based and thus independent of specific natural languages.


A knowledge representation system should have a well-defined semantics which identifies the "meaning" of concepts, relations and statements.

A concept or relation is satisfiable if it has a model. For example, an existing white swan is a model for "Swans are white".

A statement is valid if it is satisfiable for all models. For example, an existing black swan shows that "All swans are white" is not valid.

Satisfiability and validity depend on the context of statements. For example, "Unicorns are animals" can be valid in a fairy tale context but is not considered valid in normal everyday life.


Relations can be transitive, for example, "Snoopy ISA Dog", "Dog ISA Mammal" therefore "Snoopy ISA Mammal". The ISA relation should always be transitive, other relations, such as "HAS" or part-whole relations are not always transitive.

Relations require quantification. For example, does "Dogs eat dogfood" mean "all dogs eat some dogfood" or "some dogs eat all dogfood" or "some dogs eat only dogfood" or ...?

Description Logics

Description Logics is an improved version of the knowledge representation language KL-ONE. It distinguishes between terminologic knowledge (TBox), such as "poodles are dogs" or "people eat food", and assertional knowledge (Abox), such as "Snoopy is a dog" or "Snoopy is eating that piece of meat". Semantic networks can be formalized in description logics.

Tasks for knowledge representation systems

  • Acquisition: new information is integrated into the system
  • Retrieval: existing information is retrieved, query answering
  • Reasoning: checking whether concepts, relations and assertions are satisfiable/valid; checking whether the knowledge base is consistent, which means that all concepts and relations are satisfiable and all statements are valid.

    Reasoning is used for acquisition because, for example, new concepts must be integrated into the existing ISA hierarchy without creating inconsistencies. Reasoning is also used for retrieval to retrieve implicit knowledge.

    Challenges for reasoning strategies and knowledge representation

  • Complexity: the reasoning tasks must not take too much time or computing resources
  • Incomplete or uncertain knowledge must be handled by the system