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.
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.
$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.
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.
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.
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.
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.
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.
Recently we have been doing some experiments to cluster semantically similar messages, by leveraging pre-trained models so we can get something off the ground using no labelled data. Task here is…
What is Semantic Similarity? Definition of Semantic Similarity: A concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning/semantic content. ( Wikipedia, 2012e ).
The Similarity Library aims at providing developers with a library for assessing similarity both between words and sentences. This library in an extension of the JWSL (Java WordNet Similarity Library). In the current implementation, there are two categories of similarity measures between words: measures exploiting ontologies such as WordNet, MeSH or the Gene Ontology measures…
The Similarity Library aims at providing developers with a library for assessing similarity both between words and sentences. This library in an extension of the JWSL (Java WordNet Similarity Library). In the current implementation, there are two categories of similarity measures between words: measures exploiting ontologies such as WordNet, MeSH or the Gene Ontology measures…
J. Choi, A. Khlif, und E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), Seite 23--27. Online, Association for Computational Linguistics, (2020)
J. Choi, A. Khlif, und E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), Seite 23--27. Online, Association for Computational Linguistics, (2020)
S. Staab, J. Lehmann, und R. Verborgh. Companion Proceedings of the The Web Conference 2018, Seite 885--886. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2018)
D. Schlör, J. Pfister, und A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), Seite 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
D. Schlör, J. Pfister, und A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), Seite 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
S. Staab, J. Lehmann, und R. Verborgh. Companion Proceedings of the The Web Conference 2018, Seite 885--886. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2018)