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Semantic Web

About: Semantic Web is a research topic. Over the lifetime, 26987 publications have been published within this topic receiving 534275 citations. The topic is also known as: Sem Web & SemWeb.


Papers
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Journal ArticleDOI
TL;DR: The authors review some of the recent developments on applying the semantic technologies based on machine-interpretable representation formalism to the Internet of Things.
Abstract: The Internet of Things IoT has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the "Things" makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT.

510 citations

Book
01 Jan 2003
TL;DR: The Semantic Web is an extension of the current Web in which information is given well defined meaning, better enabling computers and people to work in cooperation as discussed by the authors, which is the goal of the SemEval project.
Abstract: From the Publisher: "The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."–Tim Berners-Lee, Scientific American, May 2001 This authoritative guide shows how the Semantic Web works technically and how businesses can utilize it to gain a competitive advantage Explains what taxonomies and ontologies are as well as their importance in constructing the Semantic Web Companion Web site includes further updates as the framework develops and links to related sites

505 citations

01 Jan 2009
TL;DR: TheManchester Syntax is used in the OWL 2 Primer, and this document provides the language used there; it is expected that tools will extend the Manchester Syntax for their own purposes, and tool builders may collaboratively extend the common language.
Abstract: The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents. The Manchester syntax is a user-friendly compact syntax for OWL 2 ontologies; it is frame-based, as opposed to the axiom-based other syntaxes for OWL 2. The Manchester Syntax is used in the OWL 2 Primer, and this document provides the language used there. It is expected that tools will extend the Manchester Syntax for their own purposes, and tool builders may collaboratively extend the common language.

503 citations

Book ChapterDOI
06 Nov 2005
TL;DR: In this article, the authors present a framework for introducing design patterns that facilitate or improve the techniques used during ontology lifecycle, and some distinctions are drawn between kinds of ontology design patterns.
Abstract: The paper presents a framework for introducing design patterns that facilitate or improve the techniques used during ontology lifecycle. Some distinctions are drawn between kinds of ontology design patterns. Some content-oriented patterns are presented in order to illustrate their utility at different degrees of abstraction, and how they can be specialized or composed. The proposed framework and the initial set of patterns are designed in order to function as a pipeline connecting domain modelling, user requirements, and ontology-driven tasks/queries to be executed.

502 citations

Proceedings ArticleDOI
13 Apr 1996
TL;DR: This paper presents the exploration into techniques that utilize both the topology and textual similarity between items as well as usage data collected by servers and page meta-information lke title and size.
Abstract: In its current implementation, the World-Wide Web lacks much of the explicit structure and strong typing found in many closed hypertext systems. While this property probably relates to the explosive acceptance of the Web, it further complicates the already difficult problem of identifying usable structures and aggregates in large hypertext collections. These reduced structures, or localities, form the basis for simplifying visualizations of and navigation through complex hypertext systems. Much of the previous research into identifying aggregates utilize graph theoretic algorithms based upon structural topology, i.e., the linkages between items. Other research has focused on content analysis to form document collections. This paper presents our exploration into techniques that utilize both the topology and textual similarity between items as well as usage data collected by servers and page meta-information lke title and size. Linear equations and spreading activation models are employed to arrange Web pages based upon functional categories, node types, and relevancy.

494 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023116
2022348
2021412
2020612
2019782
2018881