Knowledge Representation




What is a knowledge representation ? We  argue that the notion can best be understand in terms of five distinct roles it plays, each crucial to the task at hand:

 · A knowledge representation is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting, i.e by reasoning about the world rather than taking action in it.

· It is a set of ontological commitments i.e an answer to the question: In what terms should I think about the world?

 · 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 inference the  representation  sanctions; and

(3)            the set of inferences it recommends.

·       It is medium for pragmatically efficient computation, i.e the  computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied  by the guidance a  representation provides for organizing information so as to facilitate making the recommended inferences. 

·    It is a medium of human expression , i.e. a language in which we say  things about the world.

 Knowledge representation is an area in artificial intelligence that is concerned with how to formally "think", that is, how to use a symbol system to represent "a domain of discourse" - that which can be talked about, along with functions that may or may not be within the domain of discourse that allow inference (formalized reasoning) about the objects within the domain of discourse to occur.

 Generally speaking, some kind of logic is used both to supply a formal semantics of how reasoning functions apply to symbols in the domain of discourse, as well as to supply (depending on the particulars of the logic), operators such as quantifiers, modal operators, etc. that along with an interpretation theory, give meaning to the sentences in the logic.


  It is a medium for pragmatically efficient computation, i.e. the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a  representation provides for organizing information so as to facilitate making the recommended inferences.

 Understand the roles and acknowledging their diversity has several useful consequences.

  ·       First, each role require something slightly different from a representation ; each accordingly leads to an interesting and different we want a representation  to have it.

 ·       Second, we believe the roles provide a framework useful for characterizing a wide variety of representations. We suggest that the fundamental “mindset” of a  representation can be captured by understanding how it views each of the roles , and that doing so reveals essentials similarities and differences.

·        Third, we believe that some previous disagreements about representation are usefully disentangled when all five roles are given appropriates consideration. We demonstrate this by revisiting and dissecting the early arguments concerning frames and logics.

·       Finally, we believe that viewing representation in this way has consequences for both research and practice. For research, this view provides one direct answer to a question of fundamental  significance in the field. It also suggest adopting a broad perspective on what’s important about a representation  and it makes the case that one significant part of the representation endeavor—capturing and representation the richness of the natural world—is receiving insufficient attention.

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 trades across a number of design spaces, described in the following sections. The single most important decision to be made, however 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.

History of knowledge representation:-

In computer science, particularly artificial intelligence, a number of representations have been devised to structure information.

KR is most commonly used to refer to representations intended for processing by modern computers, and in particular, for representations consisting of explicit objects (the class of all elephants, or Clyde a certain individual), and of assertions or claims about them ('Clyde is an elephant', or 'all elephants are grey'). Representing knowledge in such explicit form enables computers to draw conclusions from knowledge already stored ('Clyde is grey').

Many KR methods were tried in the 1970s and early 1980s, such as heuristic question-answering, neural networks, theorem proving, and expert systems, with varying success. Medical diagnosis (e.g., Mycin) was a major application area, as were games such as chess.

In the 1980s formal computer knowledge representation languages and systems arose. Major projects attempted to encode wide bodies of general knowledge; for example the "Cyc" project (still ongoing) went through a large encyclopedia, encoding not the information itself, but the information a reader would need in order to understand the encyclopedia.

Through such work, the difficulty of KR came to be better appreciated. In computational linguistics, meanwhile, much larger databases of language information were being built, and these, along with great increases in computer speed and capacity, made deeper KR more feasible.

Several programming languages have been developed that are oriented to KR. Prolog developed in 1972,[1] but popularized much later, represents propositions and basic logic, and can derive conclusions from known premises. KL-ONE (1980s) is more specifically aimed at knowledge representation itself. In 1995, the Dublin Core standard of metadata was conceived.

In the electronic document world, languages were being developed to represent the structure of documents, such as SGML (from which HTML descended) and later XML. These facilitated information retrieval and data mining efforts, which have in recent years begun to relate to knowledge representation.

Development of the Semantic Web, has included development of XML-based knowledge representation languages and standards, including RDF, RDF Schema, Topic Maps, DARPA Agent Markup Language (DAML), Ontology Inference Layer (OIL), and Web Ontology Language (OWL).

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