Formal Knowledge Representation Language

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A Formal Knowledge Representation Language is a knowledge representation language that is a formal language.

References

2011

  • http://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
    • Knowledge representation (KR) and reasoning' is an area of artificial intelligence whose fundamental goal is to represent knowledge in a manner that facilitates inferencing (i.e. drawing conclusions) from knowledge. It analyzes how to formally think - how to use a symbol system to represent a domain of discourse (that which can be talked about), along with functions that allow inference (formalized reasoning) about the objects. Generally speaking, some kind of logic is used both to supply formal semantics of how reasoning functions apply to symbols in the domain of discourse, as well as to how to supply operators such as quantifiers, modal operators, etc. that, along with an interpretation theory, give meaning to the sentences in the logic. When we design a knowledge representation (and a knowledge representation system to interpret sentences in the logic in order to derive inferences from them) we have to make choices across a number of design spaces. The single most important decision to be made, is the expressivity of the KR. The more expressive, the easier and more compact it is to "say something". However, more expressive languages are harder to automatically derive inferences from. An example of a less expressive KR would be propositional logic. An example of a more expressive KR would be autoepistemic temporal modal logic. Less expressive KRs may be both complete and consistent (formally less expressive than set theory). More expressive KRs may be neither complete nor consistent. The key problem is to find a KR and a supporting reasoning system that can make the inferences your application needs within the resource constraints appropriate to the problem at hand. Recent developments in KR have been driven by the Semantic Web, and have included development of XML-based knowledge representation languages and standards, including Resource Description Framework (RDF), RDF Schema, Topic Maps, DARPA Agent Markup Language (DAML), Ontology Inference Layer (OIL), and Web Ontology Language (OWL).

2004

1995

  • (Hayes, 1995) ⇒ Patrick J. Hayes. (1995). “In Defense of Logic. In Computation and Intelligence: Collected Readings." George F. Luger, editor. AAAI Press.

1993

  • (Davis et al., 1993) ⇒ Randall Davis, Howard Shrobe, Peter Szolovits. (1993). “What Is a Knowledge Representation?.” In: AI Magazine, 14(1).
    • ABSTRACT: Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.
    • QUOTE: "What is a knowledge representation? We argue that the notion can best be understood in terms of five distinct roles that it plays, each crucial to the task at hand:
      • First, a knowledge representation is most fundamentally a surrogate, a substitute for the thing itself, that is used to enable an entity to determine consequences by thinking rather than acting, that is, by reasoning about the world rather than taking action in it.
      • Second, it is a set of ontological commitments, that is, an answer to the question, In what terms should I think about the world?
      • Third, it is a fragmentary theory of intelligent reasoning expressed in terms of three components: (1) the representation’s fundamental conception of intelligent reasoning, (2) the set of inferences that the representation sanctions, and (3) the set of inferences that it recommends.
      • Fourth, it is a medium for pragmatically efficient computation, that is, the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance that a representation provides for organizing information to facilitate making the recommended inferences.
      • Fifth, it is a medium of human expression, that is, a language in which we say things about the world."

1985

1977

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