103,025
edits
Changes
From Nordan Symposia
Jump to navigationJump to searchNew page: '''Knowledge representation''' is an issue that arises in both cognitive science and artificial intelligence. In cognitive science it is concerned with how people store and process...
'''Knowledge representation''' is an issue that arises in both [[cognitive science]] and [[artificial intelligence]]. In cognitive science it is concerned with how people store and process information. In
artificial intelligence (AI) the primary aim is to store knowledge so that programs can process it and achieve the verisimilitude of human intelligence. AI researchers have borrowed representation theories from cognitive science. Thus there are representation techniques such as frames, rules and semantic networks which have originated from theories of human information processing. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing i.e. drawing conclusions from
knowledge.
Some issues that arise in knowledge representation from an AI perspective are:
* How do people represent knowledge?
* What is the nature of knowledge and how do we represent it?
* Should a representation scheme deal with a particular domain or should it be general purpose?
* How expressive is a representation scheme?
* Should the scheme be declarative or procedural?
There has been very little top-down discussion of the KR issues and research in this area is a well aged quiltwork. There are well known problems such as "spreading activation" (this is a problem in navigating a network of nodes), "subsumption" (this is concerned with selective inheritance; e.g. an ATV can be thought of as a specialization of a car but it inherits only particular characteristics) and "classification." For example a tomato could be classified both as a fruit and a vegetable.
In the field of [[artificial intelligence]], [[problem solving]] can be simplified by an appropriate choice of ''knowledge representation''. Representing the knowledge using a given technique may enable the domain to be represented. For example Mycin, a diagnostic expert system used a rule based representation scheme. An incorrect choice would defeat the representation endeavor; the analogy is to make computations in [[Hindu-Arabic numeral system|Hindu-Arabic numeral]]s or in [[Roman numeral]]s; [[long division]] is simpler in one and harder in the other. Likewise, there is no representation that can serve all purposes or make every problem equally approachable.
==History in computer science==
In '''[[computer science]]''', particularly [[artificial intelligence]], a number of representations have been devised to structure information.
"Knowledge Representation" (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 went through a large encyclopedia, encoding not the information itself, but the information a reader would need in order to understand the encyclopedia: naive physics; notions of time, causality, motivation; commonplace objects and classes of objects. The Cyc project is managed by [[Cycorp, Inc.]]; 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 [[Resource Description Framework|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 structure|trees]], by means of which the connections among fundamental concepts and derivative concepts can be shown.
Visual representations, called a [[TheBrain Technologies Corp.|"plex" as developed by TheBrain Technologies]] are relatively new in the field of knowledge management but give the user a way to visualise how one thought or idea is connected to other ideas enabling the possibility of moving from one thought to another in order to locate required information. The approach is not without its competitors. Other visual search tools are built by [http://www.convera.com/ Convera Corporation], [http://www.kmconnection.com/pguide/KSP2000348.htm Entopia, Inc.], [http://www.epeople.com/home.shtml EPeople Inc.], and [http://www.inxight.com/ Inxight Software Inc].
==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 [[Relational model|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 [[expert systems]] is an application of [[object-oriented]] programming, with [[inheritance]] of features described by the "[[is-a]]" link. However, there has been no small amount of [[inconsistency]] in the usage of the "is-a" link: [[Ronald J. Brachman]] 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 [http://moodle.ed.uiuc.edu/wiked/index.php/Schemas 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 [[behavioral 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 the [[human mind]], which is the only known working [[intelligence (trait)|intelligence]] so far, or to represent knowledge in the form of [[human language]]. Richard L. Ballard Ph.D., for example, has developed a theory-based semantics system that is language independent, which claims to capture and reason with the same concepts and theory as people. The formula underlying theory-based semantics is: Knowledge=Theory+Information. Most all conventional applications and database systems are language-based. 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.
For this reason, various [[artificial languages]] and [[notation]]s have been proposed for representing knowledge. They are typically based on [[logic]] and [[mathematics]], and have easily parsed [[grammar]]s to ease [[machine processing]]. They usually fall into the broad domain of [[Ontology (computer science)|ontologies]].
====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]] and often space-efficiency.
[[First-order predicate calculus]] is commonly used as a mathematical basis for these systems, to avoid excessive [[complexity]]. However, even simple systems based on this simple logic can be used to represent data that is well beyond the processing capability of current computer systems: see [[computability]] for reasons.
Examples of notations:
* [[DATR]] is an example for representing [[Lexicon|lexical]] knowledge
* [[Resource Description Framework|RDF]] is a simple [[notation]] for representing relationships between and among [[object (philosophy)|object]]s
====Languages====
Examples of [[artificial language]]s used primarily for knowledge representation include:
* [[CycL]]
* [[IKRIS Knowledge Language|IKL]]
* [[KIF]]
* [[LoomLanguage|Loom]]
* [[Web Ontology Language|OWL]]
* [[KM programming language|KM]] : the Knowledge Machine ([[Frame language|frame]]-based language used for knowledge representation work)
==See also==
* [[Artificial Intelligence]]
* [[Computability logic]]
* [[Cyc project]]
* [[Description logic]]
* [[Knowledge base]]
* [[Knowledge discovery]]
* [[Knowledge management]]
* [[Knowledge representation system]]
* [[Metadata (computing)|Metadata]]
* [[Morphological analysis]]
* [[MultiNet]], Multilayered Extended Semantic Networks
* [[OpenCyc]]
* [[Protege_%28software%29]], open source system
* [[Scientific modeling]]
* [[Semantic Web]]
* [[Technoscience]]
* [[Topic Maps]]
==References==
* Amaravadi, C. S., “Knowledge Management for Administrative Knowledge,” Expert Systems, 25(2), pp 53-61, May 2005.
* [[Ronald J. Brachman]]; What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks; IEEE Computer, 16 (10); October 1983 [http://citeseer.nj.nec.com/context/177306/0]
* Jean-Luc Hainaut, Jean-Marc Hick, Vincent Englebert, Jean Henrard, Didier Roland: Understanding Implementations of IS-A Relations. ER 1996: 42-57 [http://www.informatik.uni-trier.de/~ley/db/conf/er/HainautHEHR96.html]
* Hermann Helbig: ''Knowledge Representation and the Semantics of Natural Language'', Springer, Berlin, Heidelberg, New York 2006
* Arthur B. Markman: ''Knowledge Representation'' Lawrence Erlbaum Associates, 1998
* Michael Negnevitsky: ''Artificial Intelligence, A Guide to Intelligent Systems'', Pearson Education Limited, 2002
* John F. Sowa: ''Knowledge Representation'': Logical, Philosophical, and Computational Foundations. Brooks/Cole: New York, 2000
* Adrian Walker, Michael McCord, John F. Sowa, and Walter G. Wilson: ''Knowledge Systems and Prolog'', Second Edition, Addison-Wesley, 1990
==External links==
* [http://medg.lcs.mit.edu/ftp/psz/k-rep.html What is a Knowledge Representation?] by Randall Davis and others
* [http://www.makhfi.com/KCM_intro.htm Introduction to Knowledge Modeling] by Pejman Makhfi
* [http://www.inf.unibz.it/~franconi/dl/course/ Introduction to Description Logics course] by Enrico Franconi, Faculty of Computer Science, Free University of Bolzano, Italy
* [http://www.ccl.kuleuven.ac.be/LKR/html/datr.html DATR Lexical knowledge representation language]
* [http://www.isi.edu/isd/LOOM/LOOM-HOME.html Loom Project Home Page]
* [http://www.research.att.com/sw/tools/classic/tm/ijcai-95-with-scenario.html Description Logic in Practice: A CLASSIC Application]
* [http://www.dfki.uni-kl.de/ruleml/ The Rule Markup Initiative]
* [http://moodle.ed.uiuc.edu/wiked/index.php/Schemas Schemas]
* [http://nelements.org Nelements KOS] - a generic 3d knowledge representation system
http://en.wikipedia.org/wiki/Knowledge_representation
[[Category: General Reference]]
artificial intelligence (AI) the primary aim is to store knowledge so that programs can process it and achieve the verisimilitude of human intelligence. AI researchers have borrowed representation theories from cognitive science. Thus there are representation techniques such as frames, rules and semantic networks which have originated from theories of human information processing. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing i.e. drawing conclusions from
knowledge.
Some issues that arise in knowledge representation from an AI perspective are:
* How do people represent knowledge?
* What is the nature of knowledge and how do we represent it?
* Should a representation scheme deal with a particular domain or should it be general purpose?
* How expressive is a representation scheme?
* Should the scheme be declarative or procedural?
There has been very little top-down discussion of the KR issues and research in this area is a well aged quiltwork. There are well known problems such as "spreading activation" (this is a problem in navigating a network of nodes), "subsumption" (this is concerned with selective inheritance; e.g. an ATV can be thought of as a specialization of a car but it inherits only particular characteristics) and "classification." For example a tomato could be classified both as a fruit and a vegetable.
In the field of [[artificial intelligence]], [[problem solving]] can be simplified by an appropriate choice of ''knowledge representation''. Representing the knowledge using a given technique may enable the domain to be represented. For example Mycin, a diagnostic expert system used a rule based representation scheme. An incorrect choice would defeat the representation endeavor; the analogy is to make computations in [[Hindu-Arabic numeral system|Hindu-Arabic numeral]]s or in [[Roman numeral]]s; [[long division]] is simpler in one and harder in the other. Likewise, there is no representation that can serve all purposes or make every problem equally approachable.
==History in computer science==
In '''[[computer science]]''', particularly [[artificial intelligence]], a number of representations have been devised to structure information.
"Knowledge Representation" (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 went through a large encyclopedia, encoding not the information itself, but the information a reader would need in order to understand the encyclopedia: naive physics; notions of time, causality, motivation; commonplace objects and classes of objects. The Cyc project is managed by [[Cycorp, Inc.]]; 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 [[Resource Description Framework|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 structure|trees]], by means of which the connections among fundamental concepts and derivative concepts can be shown.
Visual representations, called a [[TheBrain Technologies Corp.|"plex" as developed by TheBrain Technologies]] are relatively new in the field of knowledge management but give the user a way to visualise how one thought or idea is connected to other ideas enabling the possibility of moving from one thought to another in order to locate required information. The approach is not without its competitors. Other visual search tools are built by [http://www.convera.com/ Convera Corporation], [http://www.kmconnection.com/pguide/KSP2000348.htm Entopia, Inc.], [http://www.epeople.com/home.shtml EPeople Inc.], and [http://www.inxight.com/ Inxight Software Inc].
==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 [[Relational model|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 [[expert systems]] is an application of [[object-oriented]] programming, with [[inheritance]] of features described by the "[[is-a]]" link. However, there has been no small amount of [[inconsistency]] in the usage of the "is-a" link: [[Ronald J. Brachman]] 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 [http://moodle.ed.uiuc.edu/wiked/index.php/Schemas 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 [[behavioral 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 the [[human mind]], which is the only known working [[intelligence (trait)|intelligence]] so far, or to represent knowledge in the form of [[human language]]. Richard L. Ballard Ph.D., for example, has developed a theory-based semantics system that is language independent, which claims to capture and reason with the same concepts and theory as people. The formula underlying theory-based semantics is: Knowledge=Theory+Information. Most all conventional applications and database systems are language-based. 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.
For this reason, various [[artificial languages]] and [[notation]]s have been proposed for representing knowledge. They are typically based on [[logic]] and [[mathematics]], and have easily parsed [[grammar]]s to ease [[machine processing]]. They usually fall into the broad domain of [[Ontology (computer science)|ontologies]].
====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]] and often space-efficiency.
[[First-order predicate calculus]] is commonly used as a mathematical basis for these systems, to avoid excessive [[complexity]]. However, even simple systems based on this simple logic can be used to represent data that is well beyond the processing capability of current computer systems: see [[computability]] for reasons.
Examples of notations:
* [[DATR]] is an example for representing [[Lexicon|lexical]] knowledge
* [[Resource Description Framework|RDF]] is a simple [[notation]] for representing relationships between and among [[object (philosophy)|object]]s
====Languages====
Examples of [[artificial language]]s used primarily for knowledge representation include:
* [[CycL]]
* [[IKRIS Knowledge Language|IKL]]
* [[KIF]]
* [[LoomLanguage|Loom]]
* [[Web Ontology Language|OWL]]
* [[KM programming language|KM]] : the Knowledge Machine ([[Frame language|frame]]-based language used for knowledge representation work)
==See also==
* [[Artificial Intelligence]]
* [[Computability logic]]
* [[Cyc project]]
* [[Description logic]]
* [[Knowledge base]]
* [[Knowledge discovery]]
* [[Knowledge management]]
* [[Knowledge representation system]]
* [[Metadata (computing)|Metadata]]
* [[Morphological analysis]]
* [[MultiNet]], Multilayered Extended Semantic Networks
* [[OpenCyc]]
* [[Protege_%28software%29]], open source system
* [[Scientific modeling]]
* [[Semantic Web]]
* [[Technoscience]]
* [[Topic Maps]]
==References==
* Amaravadi, C. S., “Knowledge Management for Administrative Knowledge,” Expert Systems, 25(2), pp 53-61, May 2005.
* [[Ronald J. Brachman]]; What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks; IEEE Computer, 16 (10); October 1983 [http://citeseer.nj.nec.com/context/177306/0]
* Jean-Luc Hainaut, Jean-Marc Hick, Vincent Englebert, Jean Henrard, Didier Roland: Understanding Implementations of IS-A Relations. ER 1996: 42-57 [http://www.informatik.uni-trier.de/~ley/db/conf/er/HainautHEHR96.html]
* Hermann Helbig: ''Knowledge Representation and the Semantics of Natural Language'', Springer, Berlin, Heidelberg, New York 2006
* Arthur B. Markman: ''Knowledge Representation'' Lawrence Erlbaum Associates, 1998
* Michael Negnevitsky: ''Artificial Intelligence, A Guide to Intelligent Systems'', Pearson Education Limited, 2002
* John F. Sowa: ''Knowledge Representation'': Logical, Philosophical, and Computational Foundations. Brooks/Cole: New York, 2000
* Adrian Walker, Michael McCord, John F. Sowa, and Walter G. Wilson: ''Knowledge Systems and Prolog'', Second Edition, Addison-Wesley, 1990
==External links==
* [http://medg.lcs.mit.edu/ftp/psz/k-rep.html What is a Knowledge Representation?] by Randall Davis and others
* [http://www.makhfi.com/KCM_intro.htm Introduction to Knowledge Modeling] by Pejman Makhfi
* [http://www.inf.unibz.it/~franconi/dl/course/ Introduction to Description Logics course] by Enrico Franconi, Faculty of Computer Science, Free University of Bolzano, Italy
* [http://www.ccl.kuleuven.ac.be/LKR/html/datr.html DATR Lexical knowledge representation language]
* [http://www.isi.edu/isd/LOOM/LOOM-HOME.html Loom Project Home Page]
* [http://www.research.att.com/sw/tools/classic/tm/ijcai-95-with-scenario.html Description Logic in Practice: A CLASSIC Application]
* [http://www.dfki.uni-kl.de/ruleml/ The Rule Markup Initiative]
* [http://moodle.ed.uiuc.edu/wiked/index.php/Schemas Schemas]
* [http://nelements.org Nelements KOS] - a generic 3d knowledge representation system
http://en.wikipedia.org/wiki/Knowledge_representation
[[Category: General Reference]]
