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.
Papers published on a yearly basis
Papers
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01 Mar 1985-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
128 citations
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TL;DR: A long-term vision of an intelligent user-interface agent is presented; previous work related to futuristic encyclopedias, electronic books, decision support systems, and knowledge libraries are summarized; and current and potential research directions are outlined.
Abstract: We describe a prototype electronic encyclopedia implemented on a powerful personal computer, in which user interface, media presentation, and knowledge representation techniques are applied to improving access to a knowledge resource. In itself, an electronic encyclopedia is an important information resource, but this work also illustrates the issues and approaches for many types of electronic information retrieval environments. In the prototype we make dynamic use of the structure and semantics of the text articles and index of an existing encyclopedia, while experimenting with other forms of representation, such as simulation and videodisc images. We present a long-term vision of an intelligent user-interface agent; summarize previous work related to futuristic encyclopedias, electronic books, decision support systems, and knowledge libraries; and outline current and potential research directions.
128 citations
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30 Oct 2002TL;DR: This paper presents a specifically database-inspired approach (called DOGMA) for engineering formal ontologies, implemented as shared resources used to express agreed formal semantics for a real world domain, and claims it leads to methodological approaches that naturally extend key aspects of database modeling theory and practice.
Abstract: This paper presents a specifically database-inspired approach (called DOGMA) for engineering formal ontologies, implemented as shared resources used to express agreed formal semantics for a real world domain. We address several related key issues, such as knowledge reusability and shareability, scalability of the ontology engineering process and methodology, efficient and effective ontology storage and management, and coexistence of heterogeneous rule systems that surround an ontology mediating between it and application agents. Ontologies should represent a domain's semantics independently from "language", while any process that creates elements of such an ontology must be entirely rooted in some (natural) language, and any use of it will necessarily be through a (in general an agent's computer) language.To achieve the claims stated, we explicitly decompose ontological resources into ontology bases in the form of simple binary facts called lexons and into socalled ontological commitments in the form of description rules and constraints. Ontology bases in a logic sense, become "representationless" mathematical objects which constitute the range of a classical interpretation mapping from a first order language, assumed to lexically represent the commitment or binding of an application or task to such an ontology base. Implementations of ontologies become database-like on-line resources in the model-theoretic sense. The resulting architecture allows to materialize the (crucial) notion of commitment as a separate layer of (software agent) services, mediating between the ontology base and those application instances that commit to the ontology. We claim it also leads to methodological approaches that naturally extend key aspects of database modeling theory and practice. We discuss examples of the prototype DOGMA implementation of the ontology base server and commitment server.
128 citations
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TL;DR: Sentilo implements an approach based on the neo-Davidsonian assumption that events and situations are the primary entities for contextualizing opinions, which makes it able to distinguish holders, main topics, and sub-topics of an opinion.
Abstract: Sentilo is a model and a tool to detect holders and topics of opinion sentences. Sentilo implements an approach based on the neo-Davidsonian assumption that events and situations are the primary entities for contextualizing opinions, which makes it able to distinguish holders, main topics, and sub-topics of an opinion. It uses a heuristic graph mining approach that relies on FRED, a machine reader for the Semantic Web that leverages Natural Language Processing (NLP) and Knowledge Representation (KR) components jointly with cognitively-inspired frames. The evaluation results are excellent for holder detection (F1: 95%), very good for subtopic detection (F1: 78%), and good for topic detection (F1: 68%).
128 citations
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01 Jan 1995TL;DR: The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge based system at the conceptual level (a so called model of expertise) with a description at a formal and executable level that allows the precise and unique specification of the functionality of aknowledge based system independent of any implementation details.
Abstract: The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge based system at the conceptual level (a so called model of expertise) with a description at a formal and executable level. Thus, KARL allows the precise and unique specification of the functionality of a knowledge based system independent of any implementation details. A KARL model of expertise contains the description of domain knowledge, inference knowledge, and procedural control knowledge. For capturing these different types of knowledge, KARL provides corresponding modeling primitives based on Frame Logic and Dynamic Logic. A declarative semantics for a complete KARL model of expertise is given by a combination of these two types of logic. In addition, an operational definition of this semantics, which relies on a fixpoint approach, is given. This operational semantics defines the basis for the implementation of the KARL interpreter, which includes appropriate algorithms for efficiently executing KARL specifications. This enables the evaluation of KARL specifications by means of testing.
127 citations