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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TL;DR: An approach to employ model-based diagnosis for fault detection and localization in very large V HDL programs, by automatically generating the diagnosis model from the VHDL code and using observations about the program behavior to derive possible fault locations from the model is described.
201 citations
TL;DR: In this paper, the authors examine the possible use of description logics (DLs) as a knowledge representation and reasoning system for high-level scene interpretation, and show that aggregates can be represented by concept expressions of a description logic which provides a concrete-domain extension for quantitative temporal and spatial constraints.
201 citations
Book•
01 Mar 1991
TL;DR: Representation and models - knowledge representation, general aspects, logic and objects, situational versus analytical knowledge symbolic reasoning - search, production systems, problem solving uncertainty and belief revision - representation of uncertainty, belief revision human-machine interaction - sharing intelligence, user interfaces, advanced interaction media, knowledge acquisition.
Abstract: Representation and models - knowledge representation, general aspects, logic and objects, situational versus analytical knowledge symbolic reasoning - search, production systems, problem solving uncertainty and belief revision - representation of uncertainty, belief revision human-machine interaction - sharing intelligence, user interfaces, advanced interaction media, knowledge acquisition.
200 citations
22 Jun 2003
TL;DR: This paper is presenting a generic ontology-based user modeling architecture, (OntobUM), applied in the context of a Knowledge Management System (KMS), and it relies on a user ontology, using Semantic Web technologies, based on the IMS LIP specifications, and it is integrated in an ontological-based KMS called Ontologging.
Abstract: This paper is presenting a generic ontology-based user modeling architecture, (OntobUM), applied in the context of a Knowledge Management System (KMS). Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based systems are emerging as a natural choice for the next generation of KMSs operating in organizational, interorganizational as well as community contexts. User models, often addressed as user profiles, have been included in KMSs mainly as simple ways of capturing the user preferences and/or competencies. We extend this view by including other characteristics of the users relevant in the KM context and we explain the reason for doing this. The proposed user modeling system relies on a user ontology, using Semantic Web technologies, based on the IMS LIP specifications, and it is integrated in an ontology-based KMS called Ontologging. We are presenting a generic framework for implicit and explicit ontology-based user modeling.
200 citations
TL;DR: SALT uses its knowledge of the intended problem-solving strategy in identifying relevant domain knowledge, in detecting weaknesses in the knowledge base in order to guide its interrogation of the domain expert, in generating an expert system that can perform the task and explain its line of reasoning, and in analyzing test case coverage.
199 citations