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Journal ArticleDOI

What are ontologies, and why do we need them?

01 Jan 1999-IEEE Intelligent Systems & Their Applications (IEEE Educational Activities Department)-Vol. 14, Iss: 1, pp 20-26
TL;DR: A conceptual introduction to ontologies and their role in information systems and AI is provided and how ontologies clarify the domain's structure of knowledge and enable knowledge sharing is discussed.
Abstract: This survey provides a conceptual introduction to ontologies and their role in information systems and AI. The authors also discuss how ontologies clarify the domain's structure of knowledge and enable knowledge sharing.

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Citations
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Journal ArticleDOI
TL;DR: In this article, the authors explore the domain of international entrepreneurship research by thematically mapping and assessing the intellectual territory of the field and conclude that international entrepreneurship has several coherent thematic areas and is rich in potential for future research and theory development.

1,074 citations


Cites background or methods from "What are ontologies, and why do we ..."

  • ...…(Macpherson and Holt, 2007; Thorpe et al., 2005; Tranfield et al., 2003) with inductive methods of thematic analysis used in qualitative psychology (Braun and Clarke, 2006), and informal ontological classification (Chandrasekaran et al., 1999; Noy and McGuinness, 2001; Saab and Fonseca, 2008)....

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  • ...This is done to underpin future analysis and theory building, and to facilitate the sharing and reuse of meaningful information (Chandrasekaran et al., 1999; Noy and McGuinness, 2001; Saab and Fonseca, 2008)....

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Journal ArticleDOI
TL;DR: The aim of this paper is to summarize the applications of IoT in the healthcare industry and identify the intelligentization trend and directions of future research in this field.

501 citations

Journal ArticleDOI
Yuanjie Fan1, Yuehong Yin1, Li Da Xu, Yan Zeng1, Fan Wu1 
TL;DR: This paper presents an ontology-based automating design methodology (ADM) for smart rehabilitation systems in IoT and preliminary experiments and clinical trials demonstrate valuable information on the feasibility, rapidity, and effectiveness of the proposed methodology.
Abstract: Internet of Things (IoT) makes all objects become interconnected and smart, which has been recognized as the next technological revolution. As its typical case, IoT-based smart rehabilitation systems are becoming a better way to mitigate problems associated with aging populations and shortage of health professionals. Although it has come into reality, critical problems still exist in automating design and reconfiguration of such a system enabling it to respond to the patient's requirements rapidly. This paper presents an ontology-based automating design methodology (ADM) for smart rehabilitation systems in IoT. Ontology aids computers in further understanding the symptoms and medical resources, which helps to create a rehabilitation strategy and reconfigure medical resources according to patients' specific requirements quickly and automatically. Meanwhile, IoT provides an effective platform to interconnect all the resources and provides immediate information interaction. Preliminary experiments and clinical trials demonstrate valuable information on the feasibility, rapidity, and effectiveness of the proposed methodology.

368 citations


Cites background from "What are ontologies, and why do we ..."

  • ...It is always said that ontology “curve the world at its joints,” for it is the science of the things that exist, including the natures, the properties, the characters, and the relations of the objects in reality [12]....

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Book
31 Aug 2008
TL;DR: Part I: Basics - Knowledge Representation - Ontologies - Semantic Web - Model Driven Architecture - Modeling Spaces and Part III: Application - Using UML Tools for Ontology Modeling - MDA Based Ontology Platform: AIR.
Abstract: Part I: Basics - Knowledge Representation - Ontologies - Semantic Web - Model Driven Architecture - Modeling Spaces.- Part II: Model Driven Architecture and Ontologies - Software Engineering Approaches for Ontology Development - MDA-Based Ontology Infrastructure - Ontology Definition Metamodel - Ontology UML Profile - Mappings of MDA Based Languages and Ontologies.- Part III: Application - Using UML Tools for Ontology Modeling - MDA Based Ontology Platform: AIR - Ontology Examples.

332 citations

Proceedings Article
01 Jan 2000
TL;DR: This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what the authors need for further promotion of the research from both the AI and ED points of view.
Abstract: This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed.

295 citations


Cites methods from "What are ontologies, and why do we ..."

  • ...The concept of task ontology has been proposed by one of the authors and it serves as a theory of vocabulary/concepts used as building blocks for the modeling problem solving structure (Mizoguchi, Vanwelkenhuysen, and Ikeda, 1995; Mizoguchi, Sinitsa, and Ikeda, 1996a; Chandrasekaran, Josephson and Benjamins, 1999 )....

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References
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Journal ArticleDOI
TL;DR: This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms.

12,962 citations

Journal ArticleDOI
TL;DR: The role of ontology in supporting knowledge sharing activities is described, and a set of criteria to guide the development of ontologies for these purposes are presented, and it is shown how these criteria are applied in case studies from the design ofOntologies for engineering mathematics and bibliographic data.
Abstract: Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.

6,949 citations

Book
01 Jan 1982
TL;DR: Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field of visual perception as discussed by the authors, where the process of vision constructs a set of representations, starting from a description of the input image and culminating with three-dimensional objects in the surrounding environment, a central theme and one that has had farreaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis.
Abstract: "David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This MIT Press edition makes Marr's influential work available to a new generation of students and scientists. In Marr's framework, the process of vision constructs a set of representations, starting from a description of the input image and culminating with a description of three-dimensional objects in the surrounding environment. A central theme, and one that has had far-reaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis--in Marr's framework, the computational level, the algorithmic level, and the hardware implementation level. Now, thirty years later, the main problems that occupied Marr remain fundamental open problems in the study of perception. Vision provides inspiration for the continuing efforts to integrate knowledge from cognition and computation to understand vision and the brain."--MIT CogNet.

5,482 citations

Journal ArticleDOI
TL;DR: Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list.
Abstract: Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.

5,038 citations

Book ChapterDOI
TL;DR: In this paper, the authors consider the problem of reasoning about whether a strategy will achieve a goal in a deterministic world and present a method to construct a sentence of first-order logic which will be true in all models of certain axioms if and only if a certain strategy can achieve a certain goal.
Abstract: A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge is and how it is obtained. Thus, some of the major traditional problems of philosophy arise in artificial intelligence. More specifically, we want a computer program that decides what to do by inferring in a formal language that a certain strategy will achieve its assigned goal. This requires formalizing concepts of causality, ability, and knowledge. Such formalisms are also considered in philosophical logic. The first part of the paper begins with a philosophical point of view that seems to arise naturally once we take seriously the idea of actually making an intelligent machine. We go on to the notions of metaphysically and epistemo-logically adequate representations of the world and then to an explanation of can, causes, and knows in terms of a representation of the world by a system of interacting automata. A proposed resolution of the problem of freewill in a deterministic universe and of counterfactual conditional sentences is presented. The second part is mainly concerned with formalisms within which it can be proved that a strategy will achieve a goal. Concepts of situation, fluent, future operator, action, strategy, result of a strategy and knowledge are formalized. A method is given of constructing a sentence of first-order logic which will be true in all models of certain axioms if and only if a certain strategy will achieve a certain goal. The formalism of this paper represents an advance over McCarthy (1963) and Green (1969) in that it permits proof of the correctness of strategies that contain loops and strategies that involve the acquisition of knowledge; and it is also somewhat more concise. The third part discusses open problems in extending the formalism of part 2. The fourth part is a review of work in philosophical logic in relation to problems of artificial intelligence and a discussion of previous efforts to program ‘general intelligence’ from the point of view of this paper.

3,588 citations