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: The properties that any arbitration operator should satisfy are investigated, in the style of Alchourron, Gardenfors, and Makinson, and proposed actual operators for arbitration are proposed.
Abstract: Knowledge-based systems must be able to "intelligently" manage a large amount of information coming from different sources and at different moments in time. Intelligent systems must be able to cope with a changing world by adopting a "principled" strategy. Many formalisms have been put forward in the artificial intelligence (AI) and database (DB) literature to address this problem. Among them, belief revision is one of the most successful frameworks to deal with dynamically changing worlds. Formal properties of belief revision have been investigated by Alchourron, Gardenfors, and Makinson, who put forward a set of postulates stating the properties that a belief revision operator should satisfy. Among these properties, a basic assumption of revision is that the new piece of information is totally reliable and, therefore, must be in the revised knowledge base. Different principles must be applied when there are two different sources of information and each one has a different view of the situation-the two views contradicting each other. If we do not have any reason to consider any of the sources completely unreliable, the best we can do is to "merge" the two views in a new and consistent one, trying to preserve as much information as possible. We call this merging process arbitration. In this paper, we investigate the properties that any arbitration operator should satisfy. In the style of Alchourron, Gardenfors, and Makinson we propose a set of postulates, analyze their properties, and propose actual operators for arbitration.
194 citations
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04 Jun 2006TL;DR: A general-purpose text annotation tool called Knowtator is introduced that facilitates the manual creation of annotated corpora that can be used for evaluating or training a variety of natural language processing systems.
Abstract: A general-purpose text annotation tool called Knowtator is introduced. Knowtator facilitates the manual creation of annotated corpora that can be used for evaluating or training a variety of natural language processing systems. Building on the strengths of the widely used Protege knowledge representation system, Knowtator has been developed as a Protege plug-in that leverages Protege's knowledge representation capabilities to specify annotation schemas. Knowtator's unique advantage over other annotation tools is the ease with which complex annotation schemas (e.g. schemas which have constrained relationships between annotation types) can be defined and incorporated into use. Knowtator is available under the Mozilla Public License 1.1 at http://bionlp.sourceforge.net/Knowtator.
193 citations
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TL;DR: It is proven that a complete inference algorithm for the BACK system would be computationally intractable, and it is shown that terminological reasoning is intracted for any system using a nontrivial description language.
191 citations
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TL;DR: The explainable expert systems framework (EES), in which the focus is on capturing those design aspects that are important for producing good explanations, including justifications of the system's actions, explications of general problem-solving strategies, and descriptions of the systems' terminology, is discussed.
Abstract: The explainable expert systems framework (EES), in which the focus is on capturing those design aspects that are important for producing good explanations, including justifications of the system's actions, explications of general problem-solving strategies, and descriptions of the system's terminology, is discussed. EES was developed as part of the Strategic Computing Initiative of the US Dept. of Defense's Defense Advanced Research Projects Agency (DARPA). both the general principles from which the system was derived and how the system was derived from those principles can be represented in EES. The Program Enhancement Advisor, which is the main prototype on which the explanation work has been developed and tested, is presented. PEA is an advice system that helps users improve their Common Lisp programs by recommending transformations that enhance the user's code. How EES produces better explanations is shown. >
191 citations
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TL;DR: Although explicit knowledge cannot turn into implicit knowledge through practice, it is argued that explicit learning and practice often form efficient ways of mastering an L2 by creating opportunities for implicit learning.
Abstract: This article argues for the need to reconcile symbolist and connectionist accounts of (second) language learning by propounding nine claims, aimed at integrating accounts of the representation, processing and acquisition of second language (L2) knowledge. Knowledge representation is claimed to be possible both in the form of symbols and rules and in the form of networks with layers of hidden units representing knowledge in a distributed, subsymbolic way. Implicit learning is the construction of knowledge in the form of such networks. The strength of association between the network nodes changes in the beginning stages of learning with accumulating exposure, following a power law (automatization). Network parts may attain the status equivalent to ‘symbols’. Explicit learning is the deliberate construction of verbalizable knowledge in the form of symbols (concepts) and rules. The article argues for a nonnativist, emergentist view of first language learning and adopts its own version of what could be called a non-interface position in L2 learning: although explicit knowledge cannot turn into implicit knowledge through practice, it is argued that explicit learning and practice often form efficient ways of mastering an L2 by creating opportunities for implicit learning.
191 citations