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=== 知识表示 ===

[[File:GFO taxonomy tree.png|right|thumb|本体将知识表示为领域中的一组概念以及这些概念之间的关系。]]

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传统的AI研究的重点是'''知识表示 Knowledge Representation'''<ref name="ACM 1998"/><ref name="Russell & Norvig 2003"/> <ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/>和'''知识工程 Knowledge Engineering'''~~<ref name="Knowledge engineering">Knowledge engineering:~~<ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Nilsson 1998"/>。有些“专家系统”试图将某一小领域的专家所拥有的知识收集起来。此外，一些项目试图将普通人的“常识”收集到一个包含对世界的认知的知识的大数据库中。这些常识包括：对象、属性、类别和对象之间的关系；<ref name="Representing categories and relations">Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): <ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/></ref>情景、事件、状态和时间;<ref name="Representing time">Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): <ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Nilsson 1998"/></ref>原因和结果;<ref name="Poole, Mackworth & Goebel 1998"/>关于知识的知识(我们知道别人知道什么);和许多其他研究较少的领域。“存在的东西”的表示是本体，本体是被正式描述的对象、关系、概念和属性的集合，这样的形式可以让软件智能体能够理解它。本体的语义描述了逻辑概念、角色和个体，通常在Web本体语言中以类、属性和个体的形式实现。<ref>{{cite book |last=Sikos |first=Leslie F. |date=June 2017 |title=Description Logics in Multimedia Reasoning |url=https://www.springer.com/us/book/9783319540658 |location=Cham |publisher=Springer |isbn=978-3-319-54066-5 |doi=10.1007/978-3-319-54066-5 |url-status=live |archiveurl=https://web.archive.org/web/20170829120912/https://www.springer.com/us/book/9783319540658 |archivedate=29 August 2017 |df=dmy-all }}</ref>最常见的本体称为'''上本体 Upper Ontology''',它试图为所有其他知识提供一个基础，<ref name="Ontology"/>它充当涵盖有关特定知识领域(兴趣领域或关注领域)的特定知识的领域本体之间的中介。这种形式化的知识表示可以用于基于内容的索引和检索，<ref>{{cite journal|last1=Smoliar|first1=Stephen W.|last2=Zhang|first2=HongJiang|title=Content based video indexing and retrieval|journal=IEEE Multimedia|date=1994|volume=1|issue=2|pages=62–72|doi=10.1109/93.311653}}</ref>场景解释，<ref>{{cite journal|last1=Neumann|first1=Bernd|last2=Möller|first2=Ralf|title=On scene interpretation with description logics|journal=Image and Vision Computing|date=January 2008|volume=26|issue=1|pages=82–101|doi=10.1016/j.imavis.2007.08.013}}</ref>临床决策，<ref>{{cite journal|last1=Kuperman|first1=G. J.|last2=Reichley|first2=R. M.|last3=Bailey|first3=T. C.|title=Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations|journal=Journal of the American Medical Informatics Association|date=1 July 2006|volume=13|issue=4|pages=369–371|doi=10.1197/jamia.M2055|pmid=16622160|pmc=1513681}}</ref>知识发现(从大型数据库中挖掘“有趣的”和可操作的推论)<ref>{{cite journal|last1=MCGARRY|first1=KEN|title=A survey of interestingness measures for knowledge discovery|journal=The Knowledge Engineering Review|date=1 December 2005|volume=20|issue=1|page=39|doi=10.1017/S0269888905000408|url=https://semanticscholar.org/paper/baf7f99e1b567868a6dc6238cc5906881242da01}}</ref>等领域。<ref>{{cite conference |url= |title=Automatic annotation and semantic retrieval of video sequences using multimedia ontologies |last1=Bertini |first1=M |last2=Del Bimbo |first2=A |last3=Torniai |first3=C |date=2006 |publisher=ACM |book-title=MM '06 Proceedings of the 14th ACM international conference on Multimedia |pages=679–682 |location=Santa Barbara |conference=14th ACM international conference on Multimedia}}</ref>

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传统的AI研究的重点是'''知识表示 Knowledge Representation'''<ref name="ACM 1998"/><ref name="Russell & Norvig 2003"/> <ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/>和'''知识工程 Knowledge Engineering'''<ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Nilsson 1998"/>。有些“专家系统”试图将某一小领域的专家所拥有的知识收集起来。此外，一些项目试图将普通人的“常识”收集到一个包含对世界的认知的知识的大数据库中。这些常识包括：对象、属性、类别和对象之间的关系；<ref name="Representing categories and relations">Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): <ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/></ref>情景、事件、状态和时间;<ref name="Representing time">Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): <ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Nilsson 1998"/></ref>原因和结果;<ref name="Poole, Mackworth & Goebel 1998"/>关于知识的知识(我们知道别人知道什么);和许多其他研究较少的领域。“存在的东西”的表示是本体，本体是被正式描述的对象、关系、概念和属性的集合，这样的形式可以让软件智能体能够理解它。本体的语义描述了逻辑概念、角色和个体，通常在Web本体语言中以类、属性和个体的形式实现。<ref>{{cite book |last=Sikos |first=Leslie F. |date=June 2017 |title=Description Logics in Multimedia Reasoning |url=https://www.springer.com/us/book/9783319540658 |location=Cham |publisher=Springer |isbn=978-3-319-54066-5 |doi=10.1007/978-3-319-54066-5 |url-status=live |archiveurl=https://web.archive.org/web/20170829120912/https://www.springer.com/us/book/9783319540658 |archivedate=29 August 2017 |df=dmy-all }}</ref>最常见的本体称为'''上本体 Upper Ontology''',它试图为所有其他知识提供一个基础，<ref name="Ontology"/>它充当涵盖有关特定知识领域(兴趣领域或关注领域)的特定知识的领域本体之间的中介。这种形式化的知识表示可以用于基于内容的索引和检索，<ref>{{cite journal|last1=Smoliar|first1=Stephen W.|last2=Zhang|first2=HongJiang|title=Content based video indexing and retrieval|journal=IEEE Multimedia|date=1994|volume=1|issue=2|pages=62–72|doi=10.1109/93.311653}}</ref>场景解释，<ref>{{cite journal|last1=Neumann|first1=Bernd|last2=Möller|first2=Ralf|title=On scene interpretation with description logics|journal=Image and Vision Computing|date=January 2008|volume=26|issue=1|pages=82–101|doi=10.1016/j.imavis.2007.08.013}}</ref>临床决策，<ref>{{cite journal|last1=Kuperman|first1=G. J.|last2=Reichley|first2=R. M.|last3=Bailey|first3=T. C.|title=Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations|journal=Journal of the American Medical Informatics Association|date=1 July 2006|volume=13|issue=4|pages=369–371|doi=10.1197/jamia.M2055|pmid=16622160|pmc=1513681}}</ref>知识发现(从大型数据库中挖掘“有趣的”和可操作的推论)<ref>{{cite journal|last1=MCGARRY|first1=KEN|title=A survey of interestingness measures for knowledge discovery|journal=The Knowledge Engineering Review|date=1 December 2005|volume=20|issue=1|page=39|doi=10.1017/S0269888905000408|url=https://semanticscholar.org/paper/baf7f99e1b567868a6dc6238cc5906881242da01}}</ref>等领域。<ref>{{cite conference |url= |title=Automatic annotation and semantic retrieval of video sequences using multimedia ontologies |last1=Bertini |first1=M |last2=Del Bimbo |first2=A |last3=Torniai |first3=C |date=2006 |publisher=ACM |book-title=MM '06 Proceedings of the 14th ACM international conference on Multimedia |pages=679–682 |location=Santa Barbara |conference=14th ACM international conference on Multimedia}}</ref>

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