Topic
Knowledge representation and reasoning
About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².
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30 Jul 2005
TL;DR: HEX programs are introduced, which are nonmonotonic logic programs admitting higher-order atoms as well as external atoms, and the well-known answer-set semantics are extended to this class of programs.
Abstract: We introduce HEX programs, which are nonmonotonic logic programs admitting higher-order atoms as well as external atoms, and we extend the well-known answer-set semantics to this class of programs. Higher-order features are widely acknowledged as useful for performing meta-reasoning, among other tasks. Furthermore, the possibility to exchange knowledge with external sources in a fully declarative framework such as Answer-Set Programming (ASP) is nowadays important, in particular in view of applications in the Semantic Web area. Through external atoms, HEX programs can model some important extensions to ASP, and are a useful KR tool for expressing various applications. Finally, complexity and implementation issues for a preliminary prototype are discussed.
249 citations
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TL;DR: A knowledge representation model of prototype theory is outlined, based on work in schema theory and AI knowledge representation, and it is argued that if this model is model concepts as knowledge representations of a certain kind, it is possible to answer prototype theory's critics, but to address more fundamental issues in the theory of concepts.
249 citations
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TL;DR: The strengths and limitations of four classificatory approaches are described in terms of their ability to reflect, discover, and create new knowledge.
Abstract: THELINK BETWEEN CLASSIFICATION AND KNOWLEDGE is explored. Classification schemes have properties that enable the representation of entities and relationships in structures that reflect knowledge of the domain being classified. The strengths and limitations of four classificatory approaches are described in terms of their ability to reflect, discover, and create new knowledge. These approaches are hierarchies, trees, paradigms, and faceted analysis. Examples are provided of the way in which knowledge and the classification process affect each other.
248 citations
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TL;DR: It is identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data, but relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impediment to ontology learning and applicability.
Abstract: Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next wave of web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This survey is presented in two parts. The first part reviews the state-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as well as the problems facing such research. The second complementary survey is dedicated to ontology mapping and ontology ‘evolving’. Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extrac...
247 citations
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03 Dec 2010TL;DR: CRAM equips autonomous robots with lightweight reasoning mechanisms that can infer control decisions rather than requiring the decisions to be preprogrammed, which makes them much more flexible, reliable, and general than control programs that lack such cognitive capabilities.
Abstract: This paper describes CRAM (Cognitive Robot Abstract Machine) as a software toolbox for the design, the implementation, and the deployment of cognition-enabled autonomous robots performing everyday manipulation activities. CRAM equips autonomous robots with lightweight reasoning mechanisms that can infer control decisions rather than requiring the decisions to be preprogrammed. This way CRAM-programmed autonomous robots are much more flexible, reliable, and general than control programs that lack such cognitive capabilities. CRAM does not require the whole domain to be stated explicitly in an abstract knowledge base. Rather, it grounds symbolic expressions in the knowledge representation into the perception and actuation routines and into the essential data structures of the control programs. In the accompanying video, we show complex mobile manipulation tasks performed by our household robot that were realized using the CRAM infrastructure.
246 citations