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HISTORY The term "Knowledge Representation" (KR) is most commonly used to refer to representations intended for processing by modern Computers , and particularly 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 "; much but not all of the data is now freely available. 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 (see http://www.aaai.org/AITopics/bbhist.html#mod), 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 the electronic document world, languages were being developed to represent the structure of documents more explicitly, such as SGML and later XML . These facilitated Information Retrieval and Data Mining efforts, which have in recent years begun to relate to KR. The Web community is now especially interested in the Semantic Web , in which XML-based KR languages such as RDF , Topic Maps , and others can be used to make KR information available to Web systems LINKS AND STRUCTURES While Hyperlink s have come into widespread use, the closely related Semantic Link is not yet widely used. The Mathematical Table has been used since Babylon ian times. More recently, these tables have been used to represent the outcomes of logic operations, such as Truth Table s, which were used to study and model Boolean logic, for example. Spreadsheet s are yet another tabular representation of knowledge. Other knowledge representations are Tree s, by means of which the connections among fundamental concepts and derivative concepts can be shown. STORAGE AND MANIPULATION One problem in knowledge representation consists of how to store and manipulate Knowledge in an Information System in a formal way so that it may be used by mechanisms to accomplish a given task. Examples of applications are Expert System s, Machine Translation System s, Computer-aided Maintenance systems and Information Retrieval systems (including database front-ends). Semantic Network s may be used to represent knowledge. Each node represents a Concept and arcs are used to define Relation s between the concepts. One of the most expressive and comprehensively described knowledge representation paradigms along the lines of semantic networks is MultiNet (an acronym for Multilayered Extended Semantic Networks). From the 1960s , the Knowledge Frame or just ''frame'' has been used. Each frame has its own name and a set of attributes, or '''slots''' which contain values; for instance, the frame for ''house'' might contain a ''color'' slot, ''number of floors'' slot, etc. Using frames for wrote a paper titled "What IS-A is and isn't", wherein 29 different semantics were found in projects whose knowledge representation schemes involved an "is-a" link. Other links include the " Has-part " link. Frame structures are well-suited for the representation of schematic knowledge and stereotypical cognitive patterns. The elements of such schematic patterns are weighted unequally, attributing higher weights to the more typical elements of a schema . A pattern is activated by certain expectations: If a person sees a big bird, he or she will classify it rather as a sea eagle than a golden eagle, assuming that his or her "sea-scheme" is currently activated and his "land-scheme" is not. Frame representations are object-centered in the same sense as Semantic Network s are: All the facts and properties connected with a concept are located in one place - there is no need for costly search processes in the database. A ''script'' is a type of frame that describes what happens temporally; the usual example given is that of describing going to a Restaurant . The steps include waiting to be seated, receiving a menu, ordering, etc. The different solutions can be arranged in a so-called Semantic Spectrum with respect to their semantic expressivity. Language and notation Some people think it would be best to represent knowledge in the same way that it is represented in Human Mind , which is the only known working Intelligence so far, or to represent knowledge in the form of Human Language . Unfortunately, we don't know how knowledge is represented in the human mind, or how to manipulate human languages the same way that the human mind does it. One clue is that primates know how to use Point And Click user interfaces; thus the ''gesture-based interface'' appears to be part of our cognitive apparatus, a Modality which is not tied to verbal Language , and which exists in other Animal s besides Human s. Notation The recent fashion in knowledge representation languages is to use XML as the low-level syntax. This tends to make the Output of these KR languages easy for machines to Parse , at the expense of human Readability . for reasons. Examples of notations:
Languages Examples of Artificial Language s intended for knowledge representation include: SEE ALSO
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