Knowledge representation
Encyclopedia
Knowledge representation (KR) is an area of artificial intelligence
research aimed at representing knowledge in symbols to facilitate inferencing
from those knowledge
elements, creating new elements of knowledge. The KR can be made to be independent of the underlying knowledge model or knowledge base system (KBS) such as a semantic network
.
about elements in the KR to create new KR sentences. Logic is used to supply formal semantics
of how reasoning functions should be applied to the symbols in the KR system. Logic is also used to define how operators can process and reshape the knowledge. Examples of operators and operations include, negation, conjunction, adverbs, adjectives, quantifiers and modal operators. Interpretation theory is this logic. These elements--symbols, operators, and interpretation theory--are what give sequences of symbols meaning within a KR.
A key parameter in choosing or creating a KR is its expressivity. The more expressive a KR, the easier and more compact it is to express a fact or element of knowledge within the semantics
and grammar
of that KR. However, more expressive languages are likely to require more complex logic and algorithms to construct equivalent inferences. A highly expressive KR is also less likely to be complete and consistent
. Less expressive KRs may be both complete and consistent. Autoepistemic
temporal modal logic is a highly expressive KR system, encompassing meaningful chunks of knowledge with brief, simple symbol sequences (sentences). Propositional logic is much less expressive but highly consistent and complete and can efficiently produce inferences with minimal algorithm complexity. Nonetheless, only the limitations of an underlying knowledge base affect the ease with which inferences may ultimately be made (once the appropriate KR has been found). This is because a knowledge set may be exported from a knowledge model or knowledge base system (KBS) into different KRs, with different degrees of expressivenes, completeness, and consistency. If a particular KR is inadequate in some way, that set of problematic KR elements may be transformed by importing them into a KBS, modified and operated on to eliminate the problematic elements or augmented with additional knowledge imported from other sources, and then exported into a different, more appropriate KR.
In applying KR systems to practical problems, the complexity of the problem may exceed the resource constraints or the capabilities of the KR system. Recent developments in KR include the concept of the Semantic Web
, and 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).
There are several KR techniques such as frames, rules, tagging, and semantic networks which originated in Cognitive Science
. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to facilitate reasoning, inferencing
, or drawing conclusions. A good KR must be both declarative and procedural knowledge
. What is knowledge representation can best be understood in terms of five distinct roles it plays, each crucial to the task at hand :
Some issues that arise in knowledge representation from an AI perspective are:
There has been very little top-down discussion of the knowledge representation (KR) issues and research in this area is a well aged quillwork. 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 knowledge in some ways makes certain problems easier to solve. For example, it is easier to divide numbers represented in Hindu-Arabic numerals
than numbers represented as Roman numerals.
To gain a better understanding of why these characteristics represent a good knowledge representation, think about how an encyclopedia (e.g. Wikipedia) is structured. There are millions of articles (coverage), and they are sorted into categories, content types, and similar topics (understandable). It redirects different titles but same content to the same article (consistency). It is efficient, easy to add new pages or update existing ones, and allows users on their mobile phones and desktops to view its knowledge base.
, 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: naive physics; notions of time, causality, motivation; commonplace objects and classes of objects.
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 language
s have been developed that are oriented to KR. Prolog
developed in 1972, 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).
.
Psycholinguistics
investigates how the human mind stores and manipulates language
. Other branches of cognitive science
examine how human memory stores sound
s, sight
s, smell
s, emotion
s, procedures
, and abstract idea
s. Science has not yet completely described the internal mechanisms of the brain
to the point where they can simply be replicated by computer programmers.
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 ontologies
.
, a number of ontology languages have been developed. Most are declarative languages, and are either frame language
s, or are based on first-order logic
. Most of these languages only define an upper ontology with generic concepts, whereas the domain concepts are not part of the language definition. These languages all use special-purpose knowledge engineering because as stated by Tom Gruber, "Every ontology is a treaty- a social agreement among people with common motive in sharing." There are always many competing and differing views that make any general purpose ontology impossible. A general purpose ontology would have to be applicable in any domain and different areas of knowledge need to be unified. Gellish English
is an example of an ontological language that includes a full engineering English Dictionary.
There is a long history of work attempting to build good ontologies for a variety of task domains, including early work on an ontology for liquids , the lumped element model widely used in representing electronic circuits (e.g.), as well as ontologies for time, belief, and even programming itself. Each of these offers a way to see some part of the world.
The lumped element model, for instance, suggests that we think of circuits in terms of components with connections between them, with signals flowing instantaneously along the connections. This is a useful view, but not the only possible one. A different ontology arises if we need to attend to the electrodynamics in the device: Here signals propagate at finite speed and an object (like a resistor) that was previously viewed as a single component with an I/O behavior may now have to be thought of as an extended medium through which an electromagnetic wave flows.
Ontologies can of course be written down in a wide variety of languages and notations (e.g., logic, LISP, etc.); the essential information is not the form of that language but the content, i.e., the set of concepts offered as a way of thinking about the world. Simply put, the important part is notions like connections and components, not whether we choose to write them as predicates or LISP constructs.
The commitment we make by selecting one or another ontology can produce a sharply different view of the task at hand. Consider the difference that arises in selecting the lumped element view of a circuit rather than the electrodynamic view of the same device. As a second example, medical diagnosis viewed in terms of rules (e.g., MYCIN) looks substantially different from the same task viewed in terms of frames (e.g., INTERNIST). Where MYCIN sees the medical world as made up of empirical associations connecting symptom to disease, INTERNIST sees a set of prototypes, in particular prototypical diseases, to be matched against the case at hand.
Commitment begins with the earliest choices
The INTERNIST example also demonstrates that there is significant and unavoidable ontological commitment even at the level of the familiar representation technologies. Logic, rules, frames, etc., each embody a viewpoint on the kinds of things that are important in the world. Logic, for instance, involves a (fairly minimal) commitment to viewing the world in terms of individual entities and relations between them. Rule-based systems view the world in terms of attribute-object-value triples and the rules of plausible inference that connect them, while frames have us thinking in terms of prototypical objects.
Each of these thus supplies its own view of what is important to attend to, and each suggests, conversely, that anything not easily seen in those terms may be ignored. This is of course not guaranteed to be correct, since anything ignored may later prove to be relevant. But the task is hopeless in principle--every representation ignores something about the world--hence the best we can do is start with a good guess. The existing representation technologies supply one set of guesses about what to attend to and what to ignore. Selecting any of them thus involves a degree of ontological commitment: the selection will have a significant impact on our perception of and approach to the task, and on our perception of the world being modeled.
The commitments accumulate in layers
The ontologic commitment of a representation thus begins at the level of the representation technologies and accumulates from there. Additional layers of commitment are made as we put the technology to work. The use of frame-like structures in INTERNIST offers an illustrative example. At the most fundamental level, the decision to view diagnosis in terms of frames suggests thinking in terms of prototypes, defaults, and a taxonomic hierarchy. But prototypes of what, and how shall the taxonomy be organized?
An early description of the system shows how these questions were answered in the task at hand, supplying the second layer of commitment:
The prototypes are thus intended to capture prototypical diseases (e.g., a "classic case" of a disease), and they will be organized in a taxonomy indexed around organ systems. This is a sensible and intuitive set of choices but clearly not the only way to apply frames to the task; hence it is another layer of ontological commitment.
At the third (and in this case final) layer, this set of choices is instantiated: which diseases will be included and in which branches of the hierarchy will they appear? Ontologic questions that arise even at this level can be quite fundamental. Consider for example determining which of the following are to be considered diseases (i.e., abnormal states requiring cure): alcoholism, homosexuality, and chronic fatigue syndrome. The ontologic commitment here is sufficiently obvious and sufficiently important that it is often a subject of debate in the field itself, quite independent of building automated reasoners.
Similar sorts of decisions have to be made with all the representation technologies, because each of them supplies only a first order guess about how to see the world: they offer a way of seeing but don't indicate how to instantiate that view. As frames suggest prototypes and taxonomies but do not tell us which things to select as prototypes, rules suggest thinking in terms of plausible inferences, but don't tell us which plausible inferences to attend to. Similarly logic tells us to view the world in terms of individuals and relations, but does not specify which individuals and relations to use.
Commitment to a particular view of the world thus starts with the choice of a representation technology, and accumulates as subsequent choices are made about how to see the world in those terms.
Reminder: A KR is not a data structure
Note that at each layer, even the first (e.g., selecting rules or frames), the choices being made are about representation, not data structures. Part of what makes a language representational is that it carries meaning , i.e., there is a correspondence between its constructs and things in the external world. That correspondence in turn carries with it constraint.
A semantic net, for example, is a representation, while a graph is a data structure. They are different kinds of entities, even though one is invariably used to implement the other, precisely because the net has (should have) a semantics. That semantics will be manifest in part because it constrains the network topology: a network purporting to describe family memberships as we know them cannot have a cycle in its parent links, while graphs (i.e., data structures) are of course under no such constraint and may have arbitrary cycles.
While every representation must be implemented in the machine by some data structure, the representational property is in the correspondence to something in the world and in the constraint that
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 trees
, graph
s and hypergraph
s, by means of which the connections among fundamental concepts and derivative concepts can be shown.
Visual representations 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.
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:
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 systems, 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 relations
between the concepts.The Conceptual graph
model is probably the oldest model still alive.
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 "part-of" 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 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.
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
research aimed at representing knowledge in symbols to facilitate inferencing
Inference
Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic.Human inference Inference is the act or process of deriving logical conclusions...
from those knowledge
Knowledge
Knowledge is a familiarity with someone or something unknown, which can include information, facts, descriptions, or skills acquired through experience or education. It can refer to the theoretical or practical understanding of a subject...
elements, creating new elements of knowledge. The KR can be made to be independent of the underlying knowledge model or knowledge base system (KBS) such as a semantic network
Semantic network
A semantic network is a network which represents semantic relations among concepts. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges.- History :...
.
Overview
Knowledge Representation (KR) research involves analysis of how to accurately and effectively reason and how best to use a set of symbols to represent a set of facts within a knowledge domain. A symbol vocabulary and a system of logic are combined to enable inferencesInference
Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic.Human inference Inference is the act or process of deriving logical conclusions...
about elements in the KR to create new KR sentences. Logic is used to supply formal semantics
Semantics
Semantics is the study of meaning. It focuses on the relation between signifiers, such as words, phrases, signs and symbols, and what they stand for, their denotata....
of how reasoning functions should be applied to the symbols in the KR system. Logic is also used to define how operators can process and reshape the knowledge. Examples of operators and operations include, negation, conjunction, adverbs, adjectives, quantifiers and modal operators. Interpretation theory is this logic. These elements--symbols, operators, and interpretation theory--are what give sequences of symbols meaning within a KR.
A key parameter in choosing or creating a KR is its expressivity. The more expressive a KR, the easier and more compact it is to express a fact or element of knowledge within the semantics
Semantics
Semantics is the study of meaning. It focuses on the relation between signifiers, such as words, phrases, signs and symbols, and what they stand for, their denotata....
and grammar
Grammar
In linguistics, grammar is the set of structural rules that govern the composition of clauses, phrases, and words in any given natural language. The term refers also to the study of such rules, and this field includes morphology, syntax, and phonology, often complemented by phonetics, semantics,...
of that KR. However, more expressive languages are likely to require more complex logic and algorithms to construct equivalent inferences. A highly expressive KR is also less likely to be complete and consistent
Consistency
Consistency can refer to:* Consistency , the psychological need to be consistent with prior acts and statements* "Consistency", an 1887 speech by Mark Twain...
. Less expressive KRs may be both complete and consistent. Autoepistemic
Autoepistemic logic
The autoepistemic logic is a formal logic for the representation and reasoning of knowledge about knowledge. While propositional logic can only express facts, autoepistemic logic can express knowledge and lack of knowledge about facts....
temporal modal logic is a highly expressive KR system, encompassing meaningful chunks of knowledge with brief, simple symbol sequences (sentences). Propositional logic is much less expressive but highly consistent and complete and can efficiently produce inferences with minimal algorithm complexity. Nonetheless, only the limitations of an underlying knowledge base affect the ease with which inferences may ultimately be made (once the appropriate KR has been found). This is because a knowledge set may be exported from a knowledge model or knowledge base system (KBS) into different KRs, with different degrees of expressivenes, completeness, and consistency. If a particular KR is inadequate in some way, that set of problematic KR elements may be transformed by importing them into a KBS, modified and operated on to eliminate the problematic elements or augmented with additional knowledge imported from other sources, and then exported into a different, more appropriate KR.
In applying KR systems to practical problems, the complexity of the problem may exceed the resource constraints or the capabilities of the KR system. Recent developments in KR include the concept of the Semantic Web
Semantic Web
The Semantic Web is a collaborative movement led by the World Wide Web Consortium that promotes common formats for data on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web of unstructured documents into a "web of...
, and development of XML
XML
Extensible Markup Language is a set of rules for encoding documents in machine-readable form. It is defined in the XML 1.0 Specification produced by the W3C, and several other related specifications, all gratis open standards....
-based knowledge representation languages and standards, including Resource Description Framework
Resource Description Framework
The Resource Description Framework is a family of World Wide Web Consortium specifications originally designed as a metadata data model...
(RDF), RDF Schema
RDF Schema
RDF Schema is a set of classes with certain properties using the RDF extensible knowledge representation language, providing basic elements for the description of ontologies, otherwise called RDF vocabularies, intended to structure RDF resources...
, Topic Maps, DARPA Agent Markup Language (DAML), Ontology Inference Layer
Ontology Inference Layer
OIL can be regarded as an Ontology infrastructure for the Semantic Web. OIL is based on concepts developed in Description Logic and frame-based systems and is compatible with RDFS....
(OIL), and Web Ontology Language
Web Ontology Language
The Web Ontology Language is a family of knowledge representation languages for authoring ontologies.The languages are characterised by formal semantics and RDF/XML-based serializations for the Semantic Web...
(OWL).
There are several KR techniques such as frames, rules, tagging, and semantic networks which originated in Cognitive Science
Cognition
In science, cognition refers to mental processes. These processes include attention, remembering, producing and understanding language, solving problems, and making decisions. Cognition is studied in various disciplines such as psychology, philosophy, linguistics, and computer science...
. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to facilitate reasoning, inferencing
Inference
Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic.Human inference Inference is the act or process of deriving logical conclusions...
, or drawing conclusions. A good KR must be both declarative and procedural knowledge
Procedural knowledge
Procedural knowledge, also known as imperative knowledge, is the knowledge exercised in the performance of some task. See below for the specific meaning of this term in cognitive psychology and intellectual property law....
. What is knowledge representation can best be understood in terms of five distinct roles it plays, each crucial to the task at hand :
- A knowledge representation (KR) 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: (i) the representation's fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends.
- 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.
- It is a medium of human expression, i.e., a language in which we say things about the world."
Some issues that arise in knowledge representation from an AI perspective are:
- How do people represent knowledge?
- What is the nature of knowledge?
- Should a representation scheme deal with a particular domain or should it be general purpose?
- How expressive is a representation scheme or formal languageFormal languageA formal language is a set of words—that is, finite strings of letters, symbols, or tokens that are defined in the language. The set from which these letters are taken is the alphabet over which the language is defined. A formal language is often defined by means of a formal grammar...
? - Should the scheme be declarative or procedural?
There has been very little top-down discussion of the knowledge representation (KR) issues and research in this area is a well aged quillwork. There are well known problems such as "spreading activation
Spreading activation
Spreading activation is a method for searching associative networks, neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to...
" (this is a problem in navigating a network of nodes), "subsumption" (this is concerned with selective inheritance; e.g. an ATV
All-terrain vehicle
An all-terrain vehicle , also known as a quad, quad bike, three wheeler, or four wheeler, is defined by the American National Standards Institute as a vehicle that travels on low pressure tires, with a seat that is straddled by the operator, along with handlebars for steering control...
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
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
, problem solving
Problem solving
Problem solving is a mental process and is part of the larger problem process that includes problem finding and problem shaping. Consideredthe most complex of all intellectual functions, problem solving has been defined as higher-order cognitive process that requires the modulation and control of...
can be simplified by an appropriate choice of knowledge representation. Representing knowledge in some ways makes certain problems easier to solve. For example, it is easier to divide numbers represented in Hindu-Arabic numerals
Hindu-Arabic numeral system
The Hindu–Arabic numeral system or Hindu numeral system is a positional decimal numeral system developed between the 1st and 5th centuries by Indian mathematicians, adopted by Persian and Arab mathematicians , and spread to the western world...
than numbers represented as Roman numerals.
Characteristics
A good knowledge representation covers six basic characteristics:- Coverage, which means the KR covers a breath and depth of information. Without a wide coverage, the KR cannot determine anything or resolve ambiguities.
- Understandable by humans. KR is viewed as a natural language, so the logic should flow freely. It should support modularity and hierarchies of classes (Polar bears are bears, which are animals). It should also have simple primitives that combine in complex forms.
- Consistency. If John closed the door, it can also be interpreted as the door was closed by John. By being consistent, the KR can eliminate redundant or conflicting knowledge.
- Efficient
- Easiness for modifying and updating.
- Supports the intelligent activity which uses the knowledge base
To gain a better understanding of why these characteristics represent a good knowledge representation, think about how an encyclopedia (e.g. Wikipedia) is structured. There are millions of articles (coverage), and they are sorted into categories, content types, and similar topics (understandable). It redirects different titles but same content to the same article (consistency). It is efficient, easy to add new pages or update existing ones, and allows users on their mobile phones and desktops to view its knowledge base.
History of knowledge representation and reasoning
In computer scienceComputer science
Computer science or computing science is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems...
, particularly artificial intelligence
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
, 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
Heuristic
Heuristic refers to experience-based techniques for problem solving, learning, and discovery. Heuristic methods are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical...
question-answering, neural networks
Neural Networks
Neural Networks is the official journal of the three oldest societies dedicated to research in neural networks: International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, published by Elsevier...
, theorem proving, and expert systems, with varying success. Medical diagnosis (e.g., Mycin
Mycin
In artificial intelligence, MYCIN was an early expert system designed to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics, with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many...
) was a major application area, as were games such as chess
Chess
Chess is a two-player board game played on a chessboard, a square-checkered board with 64 squares arranged in an eight-by-eight grid. It is one of the world's most popular games, played by millions of people worldwide at home, in clubs, online, by correspondence, and in tournaments.Each player...
.
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
Cyc
Cyc is an artificial intelligence project that attempts to assemble a comprehensive ontology and knowledge base of everyday common sense knowledge, with the goal of enabling AI applications to perform human-like reasoning....
" 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: naive physics; notions of time, causality, motivation; commonplace objects and classes of objects.
Through such work, the difficulty of KR came to be better appreciated. In computational linguistics
Computational linguistics
Computational linguistics is an interdisciplinary field dealing with the statistical or rule-based modeling of natural language from a computational perspective....
, 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 language
Programming language
A programming language is an artificial language designed to communicate instructions to a machine, particularly a computer. Programming languages can be used to create programs that control the behavior of a machine and/or to express algorithms precisely....
s have been developed that are oriented to KR. Prolog
Prolog
Prolog is a general purpose logic programming language associated with artificial intelligence and computational linguistics.Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is declarative: the program logic is expressed in terms of...
developed in 1972, but popularized much later, represents propositions and basic logic, and can derive conclusions from known premises. KL-ONE
KL-ONE
KL-ONE is a well known knowledge representation system in the tradition of semantic networks and frames; that is, it is a frame language. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a...
(1980s) is more specifically aimed at knowledge representation itself. In 1995, the Dublin Core
Dublin Core
The Dublin Core metadata terms are a set of vocabulary terms which can be used to describe resources for the purposes of discovery. The terms can be used to describe a full range of web resources: video, images, web pages etc and physical resources such as books and objects like artworks...
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
HTML
HyperText Markup Language is the predominant markup language for web pages. HTML elements are the basic building-blocks of webpages....
descended) and later XML
XML
Extensible Markup Language is a set of rules for encoding documents in machine-readable form. It is defined in the XML 1.0 Specification produced by the W3C, and several other related specifications, all gratis open standards....
. These facilitated information retrieval
Information retrieval
Information retrieval is the area of study concerned with searching for documents, for information within documents, and for metadata about documents, as well as that of searching structured storage, relational databases, and the World Wide Web...
and data mining
Data mining
Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
efforts, which have in recent years begun to relate to knowledge representation.
Development of the Semantic Web
Semantic Web
The Semantic Web is a collaborative movement led by the World Wide Web Consortium that promotes common formats for data on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web of unstructured documents into a "web of...
, has included development of XML
XML
Extensible Markup Language is a set of rules for encoding documents in machine-readable form. It is defined in the XML 1.0 Specification produced by the W3C, and several other related specifications, all gratis open standards....
-based knowledge representation languages and standards, including RDF
Resource Description Framework
The Resource Description Framework is a family of World Wide Web Consortium specifications originally designed as a metadata data model...
, RDF Schema
RDF Schema
RDF Schema is a set of classes with certain properties using the RDF extensible knowledge representation language, providing basic elements for the description of ontologies, otherwise called RDF vocabularies, intended to structure RDF resources...
, Topic Maps, DARPA Agent Markup Language (DAML), Ontology Inference Layer
Ontology Inference Layer
OIL can be regarded as an Ontology infrastructure for the Semantic Web. OIL is based on concepts developed in Description Logic and frame-based systems and is compatible with RDFS....
(OIL), and Web Ontology Language
Web Ontology Language
The Web Ontology Language is a family of knowledge representation languages for authoring ontologies.The languages are characterised by formal semantics and RDF/XML-based serializations for the Semantic Web...
(OWL).
Language and notation
Some think it is best to represent knowledge in the same way that it is represented in the human mind, or to represent knowledge in the form of human languageHuman language
A human language is a language primarily intended for communication among humans. The two major categories of human languages are natural languages and constructed languages...
.
Psycholinguistics
Psycholinguistics
Psycholinguistics or psychology of language is the study of the psychological and neurobiological factors that enable humans to acquire, use, comprehend and produce language. Initial forays into psycholinguistics were largely philosophical ventures, due mainly to a lack of cohesive data on how the...
investigates how the human mind stores and manipulates language
Language
Language may refer either to the specifically human capacity for acquiring and using complex systems of communication, or to a specific instance of such a system of complex communication...
. Other branches of cognitive science
Cognitive science
Cognitive science is the interdisciplinary scientific study of mind and its processes. It examines what cognition is, what it does and how it works. It includes research on how information is processed , represented, and transformed in behaviour, nervous system or machine...
examine how human memory stores sound
Sound
Sound is a mechanical wave that is an oscillation of pressure transmitted through a solid, liquid, or gas, composed of frequencies within the range of hearing and of a level sufficiently strong to be heard, or the sensation stimulated in organs of hearing by such vibrations.-Propagation of...
s, sight
Visual perception
Visual perception is the ability to interpret information and surroundings from the effects of visible light reaching the eye. The resulting perception is also known as eyesight, sight, or vision...
s, smell
Olfaction
Olfaction is the sense of smell. This sense is mediated by specialized sensory cells of the nasal cavity of vertebrates, and, by analogy, sensory cells of the antennae of invertebrates...
s, emotion
Emotion
Emotion is a complex psychophysiological experience of an individual's state of mind as interacting with biochemical and environmental influences. In humans, emotion fundamentally involves "physiological arousal, expressive behaviors, and conscious experience." Emotion is associated with mood,...
s, procedures
Procedure (term)
A procedure is a sequence of actions or operations which have to be executed in the same manner in order to always obtain the same result under the same circumstances ....
, and abstract idea
Idea
In the most narrow sense, an idea is just whatever is before the mind when one thinks. Very often, ideas are construed as representational images; i.e. images of some object. In other contexts, ideas are taken to be concepts, although abstract concepts do not necessarily appear as images...
s. Science has not yet completely described the internal mechanisms of the brain
Human brain
The human brain has the same general structure as the brains of other mammals, but is over three times larger than the brain of a typical mammal with an equivalent body size. Estimates for the number of neurons in the human brain range from 80 to 120 billion...
to the point where they can simply be replicated by computer programmers.
Various artificial languages and notation
Mathematical notation
Mathematical notation is a system of symbolic representations of mathematical objects and ideas. Mathematical notations are used in mathematics, the physical sciences, engineering, and economics...
s have been proposed for representing knowledge. They are typically based on logic
Logic
In philosophy, Logic is the formal systematic study of the principles of valid inference and correct reasoning. Logic is used in most intellectual activities, but is studied primarily in the disciplines of philosophy, mathematics, semantics, and computer science...
and mathematics
Mathematics
Mathematics is the study of quantity, space, structure, and change. Mathematicians seek out patterns and formulate new conjectures. Mathematicians resolve the truth or falsity of conjectures by mathematical proofs, which are arguments sufficient to convince other mathematicians of their validity...
, and have easily parsed grammar
Grammar
In linguistics, grammar is the set of structural rules that govern the composition of clauses, phrases, and words in any given natural language. The term refers also to the study of such rules, and this field includes morphology, syntax, and phonology, often complemented by phonetics, semantics,...
s to ease machine processing. They usually fall into the broad domain of ontologies
Ontology (computer science)
In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between those concepts. It can be used to reason about the entities within that domain and may be used to describe the domain.In theory, an ontology is...
.
Ontology Engineering
After CycLCycL
CycL in computer science and artificial intelligence is an ontology language used by Doug Lenat's Cyc artificial intelligence project. Ramanathan V. Guha was instrumental in the design of early versions of the language. There is a close variant of CycL known as MELD.The original version of CycL was...
, a number of ontology languages have been developed. Most are declarative languages, and are either frame language
Frame language
A frame language is a metalanguage. It applies the frame concept to the structuring of language properties. Frame languages are usually software languages.-Description:...
s, or are based on first-order logic
First-order logic
First-order logic is a formal logical system used in mathematics, philosophy, linguistics, and computer science. It goes by many names, including: first-order predicate calculus, the lower predicate calculus, quantification theory, and predicate logic...
. Most of these languages only define an upper ontology with generic concepts, whereas the domain concepts are not part of the language definition. These languages all use special-purpose knowledge engineering because as stated by Tom Gruber, "Every ontology is a treaty- a social agreement among people with common motive in sharing." There are always many competing and differing views that make any general purpose ontology impossible. A general purpose ontology would have to be applicable in any domain and different areas of knowledge need to be unified. Gellish English
Gellish English
Gellish English is a variant of Gellish and is a formal language, which means that it is structured and formalised subset of natural English that is computer interpretable. Its definition includes an English dictionary of concepts that is arranged in a taxonomy and that is extended into an ontology...
is an example of an ontological language that includes a full engineering English Dictionary.
There is a long history of work attempting to build good ontologies for a variety of task domains, including early work on an ontology for liquids , the lumped element model widely used in representing electronic circuits (e.g.), as well as ontologies for time, belief, and even programming itself. Each of these offers a way to see some part of the world.
The lumped element model, for instance, suggests that we think of circuits in terms of components with connections between them, with signals flowing instantaneously along the connections. This is a useful view, but not the only possible one. A different ontology arises if we need to attend to the electrodynamics in the device: Here signals propagate at finite speed and an object (like a resistor) that was previously viewed as a single component with an I/O behavior may now have to be thought of as an extended medium through which an electromagnetic wave flows.
Ontologies can of course be written down in a wide variety of languages and notations (e.g., logic, LISP, etc.); the essential information is not the form of that language but the content, i.e., the set of concepts offered as a way of thinking about the world. Simply put, the important part is notions like connections and components, not whether we choose to write them as predicates or LISP constructs.
The commitment we make by selecting one or another ontology can produce a sharply different view of the task at hand. Consider the difference that arises in selecting the lumped element view of a circuit rather than the electrodynamic view of the same device. As a second example, medical diagnosis viewed in terms of rules (e.g., MYCIN) looks substantially different from the same task viewed in terms of frames (e.g., INTERNIST). Where MYCIN sees the medical world as made up of empirical associations connecting symptom to disease, INTERNIST sees a set of prototypes, in particular prototypical diseases, to be matched against the case at hand.
Commitment begins with the earliest choices
The INTERNIST example also demonstrates that there is significant and unavoidable ontological commitment even at the level of the familiar representation technologies. Logic, rules, frames, etc., each embody a viewpoint on the kinds of things that are important in the world. Logic, for instance, involves a (fairly minimal) commitment to viewing the world in terms of individual entities and relations between them. Rule-based systems view the world in terms of attribute-object-value triples and the rules of plausible inference that connect them, while frames have us thinking in terms of prototypical objects.
Each of these thus supplies its own view of what is important to attend to, and each suggests, conversely, that anything not easily seen in those terms may be ignored. This is of course not guaranteed to be correct, since anything ignored may later prove to be relevant. But the task is hopeless in principle--every representation ignores something about the world--hence the best we can do is start with a good guess. The existing representation technologies supply one set of guesses about what to attend to and what to ignore. Selecting any of them thus involves a degree of ontological commitment: the selection will have a significant impact on our perception of and approach to the task, and on our perception of the world being modeled.
The commitments accumulate in layers
The ontologic commitment of a representation thus begins at the level of the representation technologies and accumulates from there. Additional layers of commitment are made as we put the technology to work. The use of frame-like structures in INTERNIST offers an illustrative example. At the most fundamental level, the decision to view diagnosis in terms of frames suggests thinking in terms of prototypes, defaults, and a taxonomic hierarchy. But prototypes of what, and how shall the taxonomy be organized?
An early description of the system shows how these questions were answered in the task at hand, supplying the second layer of commitment:
- The knowledge base underlying the INTERNIST system is composed of two basic types of elements: disease entities and manifestations.... [It] also contains a...hierarchy of disease categories, organized primarily around the concept of organ systems, having at the top level such categories as "liver disease," "kidney disease," etc.
The prototypes are thus intended to capture prototypical diseases (e.g., a "classic case" of a disease), and they will be organized in a taxonomy indexed around organ systems. This is a sensible and intuitive set of choices but clearly not the only way to apply frames to the task; hence it is another layer of ontological commitment.
At the third (and in this case final) layer, this set of choices is instantiated: which diseases will be included and in which branches of the hierarchy will they appear? Ontologic questions that arise even at this level can be quite fundamental. Consider for example determining which of the following are to be considered diseases (i.e., abnormal states requiring cure): alcoholism, homosexuality, and chronic fatigue syndrome. The ontologic commitment here is sufficiently obvious and sufficiently important that it is often a subject of debate in the field itself, quite independent of building automated reasoners.
Similar sorts of decisions have to be made with all the representation technologies, because each of them supplies only a first order guess about how to see the world: they offer a way of seeing but don't indicate how to instantiate that view. As frames suggest prototypes and taxonomies but do not tell us which things to select as prototypes, rules suggest thinking in terms of plausible inferences, but don't tell us which plausible inferences to attend to. Similarly logic tells us to view the world in terms of individuals and relations, but does not specify which individuals and relations to use.
Commitment to a particular view of the world thus starts with the choice of a representation technology, and accumulates as subsequent choices are made about how to see the world in those terms.
Reminder: A KR is not a data structure
Note that at each layer, even the first (e.g., selecting rules or frames), the choices being made are about representation, not data structures. Part of what makes a language representational is that it carries meaning , i.e., there is a correspondence between its constructs and things in the external world. That correspondence in turn carries with it constraint.
A semantic net, for example, is a representation, while a graph is a data structure. They are different kinds of entities, even though one is invariably used to implement the other, precisely because the net has (should have) a semantics. That semantics will be manifest in part because it constrains the network topology: a network purporting to describe family memberships as we know them cannot have a cycle in its parent links, while graphs (i.e., data structures) are of course under no such constraint and may have arbitrary cycles.
While every representation must be implemented in the machine by some data structure, the representational property is in the correspondence to something in the world and in the constraint that
Links and structures
While hyperlinkHyperlink
In computing, a hyperlink is a reference to data that the reader can directly follow, or that is followed automatically. A hyperlink points to a whole document or to a specific element within a document. Hypertext is text with hyperlinks...
s have come into widespread use, the closely related semantic link is not yet widely used. The mathematical table
Mathematical table
Before calculators were cheap and plentiful, people would use mathematical tables —lists of numbers showing the results of calculation with varying arguments— to simplify and drastically speed up computation...
has been used since Babylon
Babylon
Babylon was an Akkadian city-state of ancient Mesopotamia, the remains of which are found in present-day Al Hillah, Babil Province, Iraq, about 85 kilometers south of Baghdad...
ian times. More recently, these tables have been used to represent the outcomes of logic operations, such as truth table
Truth table
A truth table is a mathematical table used in logic—specifically in connection with Boolean algebra, boolean functions, and propositional calculus—to compute the functional values of logical expressions on each of their functional arguments, that is, on each combination of values taken by their...
s, which were used to study and model Boolean logic, for example. Spreadsheet
Spreadsheet
A spreadsheet is a computer application that simulates a paper accounting worksheet. It displays multiple cells usually in a two-dimensional matrix or grid consisting of rows and columns. Each cell contains alphanumeric text, numeric values or formulas...
s are yet another tabular representation of knowledge. Other knowledge representations are trees
Tree structure
A tree structure is a way of representing the hierarchical nature of a structure in a graphical form. It is named a "tree structure" because the classic representation resembles a tree, even though the chart is generally upside down compared to an actual tree, with the "root" at the top and the...
, graph
Graph (data structure)
In computer science, a graph is an abstract data structure that is meant to implement the graph and hypergraph concepts from mathematics.A graph data structure consists of a finite set of ordered pairs, called edges or arcs, of certain entities called nodes or vertices...
s and hypergraph
Hypergraph
In mathematics, a hypergraph is a generalization of a graph, where an edge can connect any number of vertices. Formally, a hypergraph H is a pair H = where X is a set of elements, called nodes or vertices, and E is a set of non-empty subsets of X called hyperedges or links...
s, by means of which the connections among fundamental concepts and derivative concepts can be shown.
Visual representations 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.
Notation
The recent fashion in knowledge representation languages is to use XMLXML
Extensible Markup Language is a set of rules for encoding documents in machine-readable form. It is defined in the XML 1.0 Specification produced by the W3C, and several other related specifications, all gratis open standards....
as the low-level syntax. This tends to make the output
Output
Output is the term denoting either an exit or changes which exit a system and which activate/modify a process. It is an abstract concept, used in the modeling, system design and system exploitation.-In control theory:...
of these KR languages easy for machines to parse, at the expense of human readability
Readability
Readability is the ease in which text can be read and understood. Various factors to measure readability have been used, such as "speed of perception," "perceptibility at a distance," "perceptibility in peripheral vision," "visibility," "the reflex blink technique," "rate of work" , "eye...
and often space-efficiency.
First-order predicate calculus is commonly used as a mathematical basis for these systems, to avoid excessive complexity
Complexity
In general usage, complexity tends to be used to characterize something with many parts in intricate arrangement. The study of these complex linkages is the main goal of complex systems theory. In science there are at this time a number of approaches to characterizing complexity, many of which are...
. 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:
- DATRDATRDATR is a language for lexical knowledge representation. The lexical knowledge is encoded in a network of nodes. Each node has a set of attributes encoded with it...
is an example for representing lexicalLexiconIn linguistics, the lexicon of a language is its vocabulary, including its words and expressions. A lexicon is also a synonym of the word thesaurus. More formally, it is a language's inventory of lexemes. Coined in English 1603, the word "lexicon" derives from the Greek "λεξικόν" , neut...
knowledge - RDFResource Description FrameworkThe Resource Description Framework is a family of World Wide Web Consortium specifications originally designed as a metadata data model...
is a simple notationMathematical notationMathematical notation is a system of symbolic representations of mathematical objects and ideas. Mathematical notations are used in mathematics, the physical sciences, engineering, and economics...
for representing relationships between and among objectsObject (philosophy)An object in philosophy is a technical term often used in contrast to the term subject. Consciousness is a state of cognition that includes the subject, which can never be doubted as only it can be the one who doubts, and some object or objects that may or may not have real existence without...
Storage and manipulation
One problem in knowledge representation is how to store and manipulate knowledgeKnowledge
Knowledge is a familiarity with someone or something unknown, which can include information, facts, descriptions, or skills acquired through experience or education. It can refer to the theoretical or practical understanding of a subject...
in an information system
Information system
An information system - or application landscape - is any combination of information technology and people's activities that support operations, management, and decision making. In a very broad sense, the term information system is frequently used to refer to the interaction between people,...
in a formal way so that it may be used by mechanisms to accomplish a given task. Examples of applications are expert system
Expert system
In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert, and not by following the procedure of a developer as is the case in...
s, machine translation systems, computer-aided maintenance
Computer-aided maintenance
Computer-aided maintenance refers to systems that utilize software to organize planning, scheduling and support of maintenance and repair. A common application of such systems is the maintenance of computers, either hardware or software, themselves...
systems and information retrieval
Information retrieval
Information retrieval is the area of study concerned with searching for documents, for information within documents, and for metadata about documents, as well as that of searching structured storage, relational databases, and the World Wide Web...
systems (including database front-ends).
Semantic network
Semantic network
A semantic network is a network which represents semantic relations among concepts. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges.- History :...
s may be used to represent knowledge. Each node represents a concept
Concept
The word concept is used in ordinary language as well as in almost all academic disciplines. Particularly in philosophy, psychology and cognitive sciences the term is much used and much discussed. WordNet defines concept: "conception, construct ". However, the meaning of the term concept is much...
and arcs are used to define relations
Relational model
The relational model for database management is a database model based on first-order predicate logic, first formulated and proposed in 1969 by Edgar F...
between the concepts.The Conceptual graph
Conceptual graph
Conceptual graphs are a formalism for knowledge representation. In the first published paper on CGs, John F. Sowa used them to represent the conceptual schemas used in database systems...
model is probably the oldest model still alive.
One of the most expressive and comprehensively described knowledge representation paradigms
along the lines of semantic networks is MultiNet
MultiNet
Multilayered extended semantic networks are both a knowledge representation paradigm and a language for meaning representation of natural language expressions that has been developed by Prof. Dr. Hermann Helbig on the basis of earlier Semantic Networks.MultiNet is claimed to be one of the most...
(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
Inheritance
Inheritance is the practice of passing on property, titles, debts, rights and obligations upon the death of an individual. It has long played an important role in human societies...
of features described by the "is-a
Is-a
In knowledge representation, object-oriented programming and design, is-a or is_a or is a is a relationship where one class D is a subclass of another class B ....
" link. However, there has been no small amount of inconsistency in the usage of the "is-a" link: Ronald J. Brachman
Ronald J. Brachman
Ronald J. "Ron" Brachman is currently Vice President of Yahoo! Labs and Research Operations at Yahoo! Labs. He is also the Head of Yahoo!'s Academic Relations organization. Prior to working at Yahoo!, he worked at DARPA as a the Director of the Information Processing Technology Office , one of...
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 "part-of" 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
Semantic network
A semantic network is a network which represents semantic relations among concepts. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges.- History :...
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
Behavioral script
In the behaviorism approach to psychology, behavioral scripts are a sequence of expected behaviors for a given situation. For example, when an individual enters a restaurant they choose a table, order, wait, eat, pay the bill, and leave. People continually follow scripts which are acquired through...
is a type of frame that describes what happens temporally; the usual example given is that of describing going to a restaurant
Restaurant
A restaurant is an establishment which prepares and serves food and drink to customers in return for money. Meals are generally served and eaten on premises, but many restaurants also offer take-out and food delivery services...
. The steps include waiting to be seated, receiving a menu, ordering, etc.
The different solutions can be arranged in a so-called semantic spectrum
Semantic spectrum
The semantic spectrum is a series of increasingly precise or rather semantically expressive definitions for data elements in knowledge representations, especially for machine use.At the low end of the spectrum is a simple binding of a single word or phrase and its...
with respect to their semantic expressivity.
See also
- Commonsense knowledge base
- Personal knowledge basePersonal knowledge baseA personal knowledge base is an electronic tool used to express, capture, and later retrieve the personal knowledgeof an individual. It differs from a traditionaldatabase in that it contains subjective material particular to the owner,...
- Valuation-based system
Further reading
- Ronald J. BrachmanRonald J. BrachmanRonald J. "Ron" Brachman is currently Vice President of Yahoo! Labs and Research Operations at Yahoo! Labs. He is also the Head of Yahoo!'s Academic Relations organization. Prior to working at Yahoo!, he worked at DARPA as a the Director of the Information Processing Technology Office , one of...
; What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks; IEEE Computer, 16 (10); October 1983 - Ronald J. BrachmanRonald J. BrachmanRonald J. "Ron" Brachman is currently Vice President of Yahoo! Labs and Research Operations at Yahoo! Labs. He is also the Head of Yahoo!'s Academic Relations organization. Prior to working at Yahoo!, he worked at DARPA as a the Director of the Information Processing Technology Office , one of...
, Hector J. Levesque Knowledge Representation and Reasoning, Morgan Kaufmann, 2004 ISBN 978-1-55860-932-7 - Ronald J. BrachmanRonald J. BrachmanRonald J. "Ron" Brachman is currently Vice President of Yahoo! Labs and Research Operations at Yahoo! Labs. He is also the Head of Yahoo!'s Academic Relations organization. Prior to working at Yahoo!, he worked at DARPA as a the Director of the Information Processing Technology Office , one of...
, Hector J. Levesque (eds) Readings in Knowledge Representation, Morgan Kaufmann, 1985, ISBN 0-934613-01-X - Chein, M., Mugnier, M.-L. (2009),Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs, Springer, 2009,ISBN 978-1-84800-285-2.
- Randall Davis, Howard Shrobe, and Peter Szolovits; What Is a Knowledge Representation? AI Magazine, 14(1):17-33,1993
- Ronald FaginRonald FaginRonald Fagin is the Manager of the Foundations of Computer Science group at the IBM Almaden Research Center. He is best known for his pioneering work in database theory, finite model theory, and reasoning about knowledge...
, Joseph Y. Halpern, Yoram MosesYoram MosesYoram Moses is a Professor in the Electrical Engineering Department at the Technion - Israel Institute of Technology.Yoram Moses received a B.Sc. in mathematics from the Hebrew University of Jerusalem in 1981, and a Ph.D. in Computer Science from Stanford University in 1986...
, Moshe Y. VardiMoshe Y. VardiMoshe Ya'akov Vardi is a Professor of Computer Science at Rice University, USA. He is the Karen Ostrum George Professor in Computational Engineering, Distinguished Service Professor, and Director of the Computer and Information Technology Institute...
Reasoning About Knowledge, MIT Press, 1995, ISBN 0-262-06162-7 - Jean-Luc Hainaut, Jean-Marc Hick, Vincent Englebert, Jean Henrard, Didier Roland: Understanding Implementations of IS-A Relations. ER 1996: 42-57
- 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
- John F. SowaJohn F. SowaJohn Florian Sowa is the computer scientist who invented conceptual graphs, a graphic notation for logic and natural language, based on the structures in semantic networks and on the existential graphs of Charles S. Peirce. He is currently developing high-level "ontologies" for artificial...
: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole: New York, 2000 - Adrian Walker, Michael McCord, John F. SowaJohn F. SowaJohn Florian Sowa is the computer scientist who invented conceptual graphs, a graphic notation for logic and natural language, based on the structures in semantic networks and on the existential graphs of Charles S. Peirce. He is currently developing high-level "ontologies" for artificial...
, and Walter G. Wilson: Knowledge Systems and Prolog, Second Edition, Addison-Wesley, 1990
External links
- What is a Knowledge Representation? by Randall Davis and others
- Introduction to Knowledge Modeling by Pejman Makhfi
- Introduction to Description Logics course by Enrico Franconi, Faculty of Computer Science, Free University of Bolzano, Italy
- DATR Lexical knowledge representation language
- Loom Project Home Page
- Description Logic in Practice: A CLASSIC Application
- The Rule Markup Initiative
- Schemas
- Nelements KOS - a non-free 3d knowledge representation system