Also known as SemanticMediaWiki, SemanticMW, SMW, Semantic MW, semantic-media-wiki
software for creating, managing and sharing structured data in MediaWiki
Semantic MediaWiki (a.k.a. SMW) is a free, open-source extension to MediaWiki – the wiki software that powers Wikipedia – that lets you store and query data within the wiki's pages. Semantic MediaWiki is also a full-fledged framework with many spinoff extensions that can turn a wiki into a powerful and flexible knowledge management system. All data created within SMW can easily be published via the Semantic Web, allowing other systems to use this data seamlessly. For a better understanding of how Semantic MediaWiki works, have a look at deployed in 5 min and the Sesame, Fuseki triplestore video, or browse the wiki for a more comprehensive introduction. Semantic MediaWiki requires MediaWiki and its dependencies, such as PHP. For supported versions, see the compatibility matrix. Most of the documentation can be found on the Semantic MediaWiki wiki. A small core of documentation also comes bundled with the software itself. This documentation is minimalistic and less explanatory than what can be found on the SMW wiki. However, It is always kept up to date and applies to the version of the code it bundles with. The most critical files are linked below. This extension is tested using GitHub Actions for Continuous Integration (CI). Each time changes are pushed to the repository, GitHub Actions automatically runs a series of tests to ensure the code remains reliable and functional. INFO : This repository contains submodules. Make sure to clone with --recursive option in Git. Step 2: Ensure test container is running This repository supports "docker-compose-ci" based CI and testing for MediaWiki extensions. The "docker-compose-ci" repository has already been integrated into the Semantic MediaWiki repository as a Git submodule. It uses "Make" as main entry point and command line interface. For more information about docker-compose-ci, see tests in Semantic MediaWiki in general, see the test documentation.
Excerpt from the source-code README · 7,321 chars · not written by Vinony
via Wikidata · CC0
via Wikidata sitelinks · CC0
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).