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: A method is proposed here, one which recognizes six very general classes of inference, which are not dependent on individual knowledge structures, but instead rely on patterns of connectivity between concepts.
133 citations
TL;DR: It turned out that almost all implemented KL-ONE systems such as BACK, KL-TWO, LOOM, NIKL, SB-ONE use sound but incomplete algorithms.
Abstract: The knowledge representation system KL-ONE first appeared in 1977. Subsequently many systems based on the idea of KL-ONE have been built. The formal model-theoretic semantics which has been introduced for KL-ONE languages [9] provides means for investigating soundness and completeness of inference algorithms. It turned out that almost all implemented KL-ONE systems such as BACK, KL-TWO, LOOM, NIKL, SB-ONE use sound but incomplete algorithms.
133 citations
01 Mar 1980
TL;DR: The material in this report discusses the problems addressed in knowledge representation research in AI and suggests some ways of comparing the various representation schemes.
Abstract: : This report is the section of the Handbook of Artificial Intelligence about knowledge representation research. The Handbook is a compendium of articles about AI ideas, techniques, and systems intended for non-AI scientists, engineers, and students. The material in this report discusses the problems addressed in knowledge representation research in AI and suggests some ways of comparing the various representation schemes. Additional articles describe the AI representation techniques: logic, procedural representations, semantic nets, production systems, direct (analogical) representations, semantic primitives, and frames.
133 citations
TL;DR: The role of case-based reasoning is presented in this paper as the collection of evidence for evidence-based medical practice and enhances the system by conferring an ability to learn from experience, and thus improve results over time.
Abstract: This paper presents the CARE-PARTNER system. Functionally, it offers via the WWW knowledge-support assistance to clinicians responsible for the long-term follow-up of stem-cell post-transplant patient care. CARE-PARTNER aims at implementing the concept of evidence-based medical practice, which recommends the practice of medicine based on proven and validated knowledge. From an artificial intelligence viewpoint, it proposes a multimodal reasoning framework for the cooperation of case-based reasoning, rule-based reasoning and information retrieval to solve problems. The role of case-based reasoning is presented in this paper as the collection of evidence for evidence-based medical practice. Case-based reasoning permits to refine and complete the knowledge of the system. It enhances the system by conferring an ability to learn from experience, and thus improve results over time.
133 citations
TL;DR: The approach to conceptual modelling is presented, which is based on knowledge representation, and it is shown how it meets the representation and reasoning needs, and the relative merits of the approach are discussed.
Abstract: Representing objects and their interactions can be quite challenging when an application requires many complicated, interconnected objects that are restricted in how they can be instantiated. In this paper, we present our approach to conceptual modelling. We have used this approach with success in a number of applications, the largest of which is the PROSE family of configurators. PROSE was first deployed in 1990 and has been used to configure over 4 billion dollars worth of AT&T and Lucent telecommunications equipment. We will discuss our approach to conceptual modelling, which is based on knowledge representation, show how it meets our representation and reasoning needs, and then discuss the relative merits of the approach.2
133 citations