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{{Short description|Artificial intelligence data structure}}
'''Frames''' are an [[artificial intelligence]] [[data structure]] used to divide [[knowledge]] into substructures by representing "[[stereotype]]d situations".
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Frames are also an extensive part of [[knowledge representation and reasoning]] schemes. They were originally derived from [[semantic network]]s and are therefore part of structure-based [[Knowledge representation and reasoning|knowledge representations]].
According to [[Stuart J. Russell|Russell]] and [[Peter Norvig|Norvig]]'s ''[[Artificial Intelligence: A Modern Approach]]'', structural representations assemble "
== Frame structure ==
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The frame contains information on how to use the frame, what to expect next, and what to do when these expectations are not met.
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== Features and advantages ==
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A frame's terminals are already filled with default values, which is based on how the [[Mind|human mind]] works.
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== Example ==
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Worth noticing here is the easy analogical reasoning (comparison) that can be done between a boy and a monkey just by having similarly named slots.
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The name of the OWL language itself provides a good example of the value of a Semantic Web. If one were to search for "OWL" using the Internet today most of the pages retrieved would be on the bird [[Owl]] rather than the standard [[Web Ontology Language|OWL]]. With a Semantic Web it would be possible to specify the concept "Web Ontology Language" and the user would not need to worry about the various possible acronyms or synonyms as part of the search. Likewise, the user would not need to worry about homonyms crowding the search results with irrelevant data such as information about birds of prey as in this simple example.
In addition to OWL, various standards and technologies that are relevant to the Semantic Web and were influenced by Frame languages include [[Ontology Inference Layer|OIL]] and [[DARPA Agent Markup Language|DAML]]. The [[Protégé (software)|Protege]] Open Source software tool from Stanford University provides an ontology editing capability that is built on OWL and has the full capabilities of a classifier. However it ceased to explicitly support frames as of version 3.5 (which is maintained for those preferring frame orientation), the version current in 2017 being 5. The justification for moving from explicit frames being that OWL DL is more expressive and "industry standard".<ref>{{cite web|last=Horridge|first=Mathew|title=Protégé OWL Tutorial A step-by-step guide to modeling in OWL using the popular Protégé OWL tools.|url=http://130.88.198.11/tutorials/protegeowltutorial/|work
===Comparison of frames and objects===
Frame languages have a significant overlap with [[Object-oriented programming|object-oriented]] languages. The terminologies and goals of the two communities were different but as they moved from the academic world and labs to the commercial world developers tended to not care about philosophical issues and focused primarily on specific capabilities, taking the best from either camp regardless of where the idea began. What both paradigms have in common is a desire to reduce the distance between concepts in the real world and their implementation in software. As such both [[paradigm]]s arrived at the idea of representing the primary software objects in [[Taxonomy|taxonomies]] starting with very general types and progressing to more specific types.
The following table illustrates the [[correlation]] between standard terminology from the object-oriented and frame language communities:
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The most notable of the more formal approaches was the [[KL-ONE]] language.<ref>{{cite journal|last=Brachman|first=Ron|title=A Structural Paradigm for Representing Knowledge|journal=Bolt, Beranek, and Neumann Technical Report|year=1978|issue=3605|url=http://www.dtic.mil/docs/citations/ADA056524}}{{dead link|date=June 2022|bot=medic}}{{cbignore|bot=medic}}</ref> KL-ONE later went on to spawn several subsequent Frame languages. The formal semantics of languages such as KL-ONE gave these frame languages a new type of automated reasoning capability known as the [[Deductive classifier|classifier]]. The classifier is an engine that analyzes the various declarations in the frame language: the definition of sets, subsets, relations, etc. The classifier can then automatically deduce various additional relations and can detect when some parts of a model are inconsistent with each other. In this way many of the tasks that would normally be executed by forward or backward chaining in an inference engine can instead be performed by the classifier.<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|doi=10.1109/64.87683|pages=41–46|s2cid=29575443 }}</ref>
This technology is especially valuable in dealing with the Internet. It is an interesting result that the formalism of languages such as KL-ONE can be most useful dealing with the highly informal and unstructured data found on the Internet. On the Internet it is simply not feasible to require all systems to standardize on one data model. It is inevitable that terminology will be used in multiple inconsistent forms. The automatic classification capability of the classifier engine provides AI developers with a powerful toolbox to help bring order and consistency to a very inconsistent collection of data (i.e., the Internet). The vision for an enhanced Internet, where pages are ordered not just by text keywords but by classification of concepts is known as the [[Semantic Web]]. Classification technology originally developed for Frame languages is a key enabler of 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 |issue=5 |pages=34–43 |url-status=dead |archive-url=https://web.archive.org/web/20130424071228/http://www.cs.umd.edu/~golbeck/LBSC690/SemanticWeb.html |archive-date=2013-04-24 |url-access=subscription }}</ref><ref>{{cite web|last=Horridge|first=Mathew|title=Protégé OWL Tutorial A step-by-step guide to modelling in OWL using the popular Protégé OWL tools.|url=http://130.88.198.11/tutorials/protegeowltutorial/|work
== See also ==
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*[[Knowledge base]]
*[[Knowledge-based system]]
*[[Knowledge graph]]
*[[Ontology language]]
*[[Predicate (disambiguation)|Predicate]]
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* Russell, Stuart J.; Norvig, Peter (2010), ''Artificial Intelligence: A Modern Approach'' (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, {{ISBN|0-13-604259-7}}, ch. 1.
* Marvin Minsky, [http://web.media.mit.edu/~minsky/papers/Frames/frames.html A Framework for Representing Knowledge], MIT-AI Laboratory Memo 306, June, 1974.
* Daniel G. Bobrow, Terry Winograd, [ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/76/581/CS-TR-76-581.pdf An Overview of KRL, A Knowledge Representation Language]{{dead link|date=May 2025|bot=medic}}{{cbignore|bot=medic}}
* R. Bruce Roberts and Ira P. Goldstein, [https://web.archive.org/web/20170706131617/ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-408.pdf The FRL Primer], 1977
* R. Bruce Roberts and Ira P. Goldstein, [https://web.archive.org/web/20170706131620/ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-409.pdf The FRL Manual], 1977
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