The recent release of this open-source project, LlamaFS, addresses the challenges associated with traditional file management systems, particularly in the context of overstuffed download folders, inefficient file organization, and the limitations of knowledge-based organization. These issues arise due to the manual nature of file sorting, which often leads to inconsistent structures and difficulty finding specific files. The disorganization in the file system hampers productivity and makes it challenging to locate important files quickly.
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.
2012. Metadata Statistics for a Large Web Corpus
ABSTRACT
We provide an analysis of the adoption of metadata standards on the Web based a large crawl of the Web. In particular, we look at what forms of syntax and vocabularies publishers are using to mark up data inside HTML pages. We also describe the process that we have followed and the difficulties involved in web data extraction.
Abstract. In order to support web applications to understand the content of HTML pages an increasing number of websites have started to annotate structured data within their pages using markup formats such as Microdata, RDFa, Microformats. The annotations are used by Google, Yahoo!, Yandex, Bing and Facebook to enrich search results and to display entity descriptions within their applications. In this paper, we present a series of publicly accessible Microdata, RDFa, Microformats datasets that we have extracted from three large web corpora dating from 2010, 2012 and 2013.
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.
This repository contains documentation and documents related to managing change in a repository of linked data entities. Linked data entities include, but are not limited to, authoritative data, controlled vocabularies, bibliographic data, etc.