Knowledge-based systems: Difference between revisions

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Another advancement was the development of special purpose automated reasoning systems called classifiers. Rather than statically declare the subsumption relations in a knowledge-base a classifier allows the developer to simply declare facts about the world and let the classifier deduce the relations. In this way a classifier also can play the role of an inference engine.<ref>{{cite journal|last=MacGregor|first=Robert|title=Using a description classifier to enhance knowledge representation|journal=IEEE Expert|date=June 1991|volume=6|issue=3|url=http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=87683&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D87683|accessdate=10 November 2013|doi=10.1109/64.87683|pages=41–46}}</ref>
Another advancement was the development of special purpose automated reasoning systems called classifiers. Rather than statically declare the subsumption relations in a knowledge-base a classifier allows the developer to simply declare facts about the world and let the classifier deduce the relations. In this way a classifier also can play the role of an inference engine.<ref>{{cite journal|last=MacGregor|first=Robert|title=Using a description classifier to enhance knowledge representation|journal=IEEE Expert|date=June 1991|volume=6|issue=3|url=http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=87683&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D87683|accessdate=10 November 2013|doi=10.1109/64.87683|pages=41–46}}</ref>


The most recent advancement of knowledge-based systems has been to adopt the technologies for the development of systems that use the Internet. The Internet often has to deal with complex, unstructured data that can't be relied on to fit a specific data model. The technology of knowledge-based systems and especially the ability to classify objects on demand is ideal for such systems. The model for these kinds of knowledge-based Internet systems is known as the [[Semantic Web]].<ref>{{cite journal|last=Berners-Lee|first=Tim |author2=James Hendler |author3=Ora Lassila|title=The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities|journal=Scientific American|date=May 17, 2001|url=http://www.cs.umd.edu/~golbeck/LBSC690/SemanticWeb.html|doi=10.1038/scientificamerican0501-34|volume=284|pages=34–43}}</ref>
The most recent advancement of knowledge-based systems has been to adopt the technologies for the development of systems that use the internet. The internet often has to deal with complex, unstructured data that can't be relied on to fit a specific data model. The technology of knowledge-based systems and especially the ability to classify objects on demand is ideal for such systems. The model for these kinds of knowledge-based Internet systems is known as the [[Semantic Web]].<ref>{{cite journal|last=Berners-Lee|first=Tim |author2=James Hendler |author3=Ora Lassila|title=The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities|journal=Scientific American|date=May 17, 2001|url=http://www.cs.umd.edu/~golbeck/LBSC690/SemanticWeb.html|doi=10.1038/scientificamerican0501-34|volume=284|pages=34–43}}</ref> The term is broad, and is used to refer to many kinds of systems; examples include IBM's Watson<ref>{{Cite web|url=http://www.ibm.com/watson/what-is-watson.html|title=What is IBM Watson?|website=www.ibm.com|language=en-US|access-date=2016-07-12}}</ref> and the Wolfram Language<ref>{{Cite web|url=http://www.wolfram.com/language/|title=Wolfram Language for Knowledge-Based Programming|website=www.wolfram.com|access-date=2016-07-12}}</ref>.


==See also==
==See also==

Revision as of 19:40, 12 July 2016

A knowledge-based system (KBS) is a

subsumption ontology. The inference engine represents logical assertions and conditions about the world, usually represented via IF-THEN rules.[1]

Knowledge-Based systems were first developed by

expert systems
. In fact the term is often used synonymously with expert systems. The difference is in the view taken to describe the system. Expert system refers to the type of task the system is trying to solve, to replace or aid a human expert in a complex task. Knowledge-based system refers to the architecture of the system, that it represents knowledge explicitly rather than as procedural code. While the earliest knowledge-based systems were almost all expert systems, the same tools and architectures can and have since been used for a whole host of other types of systems. I.e., virtually all expert systems are knowledge-based systems but many knowledge-based systems are not expert systems.

The first knowledge-based systems were rule based expert systems. One of the most famous was Mycin a program for medical diagnosis. These early expert systems represented facts about the world as simple assertions in a flat database and used rules to reason about and as a result add to these assertions. Representing knowledge explicitly via rules had several advantages:

  1. Acquisition & Maintenance. Using rules meant that domain experts could often define and maintain the rules themselves rather than via a programmer.
  2. Explanation. Representing knowledge explicitly allowed systems to reason about how they came to a conclusion and use this information to explain results to users. For example, to follow the chain of inferences that led to a diagnosis and use these facts to explain the diagnosis.
  3. Reasoning. Decoupling the knowledge from the processing of that knowledge enabled general purpose inference engines to be developed. These systems could develop conclusions that followed from a data set that the initial developers may not have even been aware of.[2]

As knowledge-based systems became more complex the techniques used to represent the knowledge base became more sophisticated. Rather than representing facts as assertions about data, the knowledge-base became more structured, representing information using similar techniques to object-oriented programming such as hierarchies of classes and subclasses, relations between classes, and behavior of objects. As the knowledge base became more structured reasoning could occur both by independent rules and by interactions within the knowledge base itself. For example, procedures stored as demons on objects could fire and could replicate the chaining behavior of rules.[3]

Another advancement was the development of special purpose automated reasoning systems called classifiers. Rather than statically declare the subsumption relations in a knowledge-base a classifier allows the developer to simply declare facts about the world and let the classifier deduce the relations. In this way a classifier also can play the role of an inference engine.[4]

The most recent advancement of knowledge-based systems has been to adopt the technologies for the development of systems that use the internet. The internet often has to deal with complex, unstructured data that can't be relied on to fit a specific data model. The technology of knowledge-based systems and especially the ability to classify objects on demand is ideal for such systems. The model for these kinds of knowledge-based Internet systems is known as the Semantic Web.[5] The term is broad, and is used to refer to many kinds of systems; examples include IBM's Watson[6] and the Wolfram Language[7].

See also

References

  1. ^ Smith, Reid (May 8, 1985). "Knowledge-Based Systems Concepts, Techniques, Examples" (PDF). http://www.reidgsmith.com. Schlumberger-Doll Research. Retrieved 9 November 2013. {{cite web}}: External link in |work= (help)
  2. ISBN 0-201-10686-8
    .
  3. ^ Mettrey, William (1987). "An Assessment of Tools for Building Large Knowledge- BasedSystems". AI Magazine. 8 (4).
  4. doi:10.1109/64.87683
    . Retrieved 10 November 2013.
  5. doi:10.1038/scientificamerican0501-34
    .
  6. ^ "What is IBM Watson?". www.ibm.com. Retrieved 2016-07-12.
  7. ^ "Wolfram Language for Knowledge-Based Programming". www.wolfram.com. Retrieved 2016-07-12.

Further reading

  • Rajendra, Akerkar; Sajja, Priti (2009). Knowledge-Based Systems. Jones & Bartlett Learning.
    ISBN 9780763776473
    .

Template:Computable knowledge

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