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Ontology (information science)

About: Ontology (information science) is a(n) research topic. Over the lifetime, 57081 publication(s) have been published within this topic receiving 869118 citation(s). The topic is also known as: vocabulary & computational ontology.

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Journal ArticleDOI
Thomas R. Gruber1Institutions (1)
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.

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Abstract: To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.

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12,418 citations


2


Journal ArticleDOI
Thomas R. Gruber1Institutions (1)
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.

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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.

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6,651 citations


01 Jan 2002-
TL;DR: An ontology defines a common vocabulary for researchers who need to share information in a domain that includes machine-interpretable definitions of basic concepts in the domain and relations among them.

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Abstract: 1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

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4,660 citations


01 Jan 2004-
TL;DR: This document provides an introduction to OWL by informally describing the features of each of the sublanguages of OWL, the Web Ontology Language by providing additional vocabulary along with a formal semantics.

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Abstract: The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. OWL has three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full. This document is written for readers who want a first impression of the capabilities of OWL. It provides an introduction to OWL by informally describing the features of each of the sublanguages of OWL. Some knowledge of RDF Schema is useful for understanding this document, but not essential. After this document, interested readers may turn to the OWL Guide for more detailed descriptions and extensive examples on the features of OWL. The normative formal definition of OWL can be found in the OWL Semantics and Abstract Syntax. Status of this document OWL Web Ontology Language Overview https://www.w3.org/TR/owl-features/ 1 de 14 09/05/2017 08:32 a.m. This document has been reviewed by W3C Members and other interested parties, and it has been endorsed by the Director as a W3C Recommendation. W3C's role in making the Recommendation is to draw attention to the specification and to promote its widespread deployment. This enhances the functionality and interoperability of the Web. This is one of six parts of the W3C Recommendation for OWL, the Web Ontology Language. It has been developed by the Web Ontology Working Group as part of the W3C Semantic Web Activity (Activity Statement, Group Charter) for publication on 10 February 2004. The design of OWL expressed in earlier versions of these documents has been widely reviewed and satisfies the Working Group's technical requirements. The Working Group has addressed all comments received, making changes as necessary. Changes to this document since the Proposed Recommendation version are detailed in the change log. Comments are welcome at public-webont-comments@w3.org (archive) and general discussion of related technology is welcome at www-rdf-logic@w3.org (archive). A list of implementations is available. The W3C maintains a list of any patent disclosures related to this work. This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.

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4,038 citations


Journal ArticleDOI
Steven Maere1, Karel Heymans1, Martin Kuiper1Institutions (1)
15 Aug 2005-Bioinformatics
TL;DR: The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine whichGene Ontology terms are significantly overrepresented in a set of genes.

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Abstract: Summary: The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results. Availability: http://www.psb.ugent.be/cbd/papers/BiNGO/ Contact: martin.kuiper@psb.ugent.be

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3,519 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202235
20211,848
20202,248
20192,562
20182,714
20172,682

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Topic's top 5 most impactful authors

Mark A. Musen

174 papers, 12K citations

Barry Smith

172 papers, 10.3K citations

Asunción Gómez-Pérez

143 papers, 9.1K citations

Ian Horrocks

141 papers, 13.3K citations

Riichiro Mizoguchi

119 papers, 3.1K citations