RDFa is an extension to HTML5 that helps you markup things like People, Places, Events, Recipes and Reviews. Search Engines and Web Services use this markup to generate better search listings and give you better visibility on the Web, so that people can find your website more easily.
Tim Berners-Lee
Date: 2007-10-23, last change: $Date: 2021/11/01 10:16:02 $
Status: personal view only. Editing status: draft. Written in response to another round of circular discussions of web architecture.
The Web is designed to support flexible exploration of information by human users and by automated agents. For such exploration to be productive, information published by many different sources and for a variety of purposes must be comprehensible to a wide range of Web client software, and to users of that software.
HTTP and other Web technologies can be used to deploy resource representations that are self-describing: information about the encodings used for each representation is provided explicitly within the representation. Starting with a URI, there is a standard algorithm that a user agent can apply to retrieve and interpret such representations. Furthermore, representations can be what we refer to as grounded in the Web, by ensuring that specifications required to interpret them are determined unambiguously based on the URI, and that explicit references connect the pertinent specifications to each other. Web-grounding ensures that the specifications needed to interpret information on the Web can be identified unambiguously. When such self-describing, Web-grounded resources are linked together, the Web as a whole can support reliable, ad hoc discovery of information.
This finding describes how document formats, markup conventions, attribute values, and other data formats can be designed to facilitate the deployment of self-describing, Web-grounded Web content.
The Web Data Commons project extracts structured data from the Common Crawl, the largest web corpus available to the public, and provides the extracted data for public download in order to support researchers and companies in exploiting the wealth of information that is available on the Web.
More and more websites have started to embed structured data describing products, people, organizations, places, and events into their HTML pages using markup standards such as Microdata, JSON-LD, RDFa, and Microformats. The Web Data Commons project extracts this data from several billion web pages. So far the project provides 11 different data set releases extracted from the Common Crawls 2010 to 2022. The project provides the extracted data for download and publishes statistics about the deployment of the different formats.
$Date: 2013-03-01 15:54:47 $
The content of the vocabulary prefixes, to be included in the RDFa 1.1 Default Profile, is defined based on the general usage of those vocabularies on the Semantic Web. This general usage is established using search crawl data, courtesy of Sindice and of Yahoo!. This page describes the methodology used during crawls as well as the possible post-processing steps.
This document describes how a Dublin Core metadata description set can be encoded in HTML/XHTML <meta> and <link> elements. It is an HTML meta data profile, as defined by the HTML specification.
HTML microdata [MICRODATA] is an extension to HTML used to embed machine-readable data into HTML documents. Whereas the microdata specification describes a means of markup, the output format is JSON. This specification describes processing rules that may be used to extract RDF [RDF11-CONCEPTS] from an HTML document containing microdata.
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D. Schlör, J. Pfister, и A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), стр. 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
S. Staab, J. Lehmann, и R. Verborgh. Companion Proceedings of the The Web Conference 2018, стр. 885--886. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2018)
J. Choi, A. Khlif, и E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), стр. 23--27. Online, Association for Computational Linguistics, (2020)
J. Choi, A. Khlif, и E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), стр. 23--27. Online, Association for Computational Linguistics, (2020)