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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TL;DR: It is argued that cardinality restrictions on concepts are of importance in applications such as configuration of technical systems, an application domain of description logics systems that is currently gaining in interest and shows that including such restrictions in the description language leaves the important inference problems such as instance testing decidable.
130 citations
TL;DR: The effectiveness of taxonomic reasoning techniques as an active support to knowledge acquisition and conceptual schema design is shown and an extended formalism and taxonomic inference algorithms for models giving prominence to attributes are given.
Abstract: Taxonomic reasoning is a typical task performed by many AI knowledge representation systems In this paper, the effectiveness of taxonomic reasoning techniques as an active support to knowledge acquisition and conceptual schema design is shown The idea developed is that by extending conceptual models with defined concepts and giving them rigorous logic semantics, it is possible to infer isa relationships between concepts on the basis of their descriptions From a theoretical point of view, this approach makes it possible to give a formal definition for consistency and minimality of a conceptual schema From a pragmatic point of view it is possible to develop an active environment that allows automatic classification of a new concept in the right position of a given taxonomy, ensuring the consistency and minimality of a conceptual schema A formalism that includes the data semantics of models giving prominence to type constructors (E/R, TAXIS, GALILEO) and algorithms for taxonomic inferences are presented: their soundness, completeness, and tractability properties are proved Finally, an extended formalism and taxonomic inference algorithms for models giving prominence to attributes (FDM, IFO) are given
130 citations
IBM1
TL;DR: A general semantic model of knowledge is introduced, to allow reasoning about statements such as «He knows that I know whether or not she knows whether or whether it is raining».
Abstract: A general semantic model of knowledge is introduced, to allow reasoning about statements such as «He knows that I know whether or not she knows whether or not it is raining». This approach more naturally models a state of knowledge than previous proposals (including Kripke structures). Using this notion of model, a model theory for knowledge is developed. This theory enables one to interpret the notion of a «finite amount of information»
130 citations
TL;DR: An extended version of propositional calculus is developed and is demonstrated to be useful for nonmonotonic reasoning, dealing with conflicting beliefs and for coping with inconsistency generated by unreliable knowledge sources.
130 citations
01 Nov 1993
TL;DR: A generic reasoning method that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning is described.
Abstract: Problem solving in weak theory domains should compensate for the lack of strong theories by combining the various other knowledge types involved. Such methods should be able to effectively combine general domain knowledge with specific case knowledge. A method is described that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning. A generic reasoning method — captured in what is called the Activate-explain-focus cycle — is able to utilise a rich knowledge model in producing context-dependent explanations. A specialisation of this method for each of the main subprocesses of case-based reasoning is presented, and illustrated with examples.
129 citations