OSW Introduction

Item:OSWdb485a954a88465287b341d2897a84d6
OSW Introduction [OSWdb485a954a88465287b341d2897a84d6]
ID OSWdb485a954a88465287b341d2897a84d6
UUID db485a95-4a88-4652-87b3-41d2897a84d6
Label OSW Introduction
Machine compatible name OswIntroduction
Statements (outgoing)
Statements (incoming)
Keywords

Description

Introduction into the open-source software stack OpenSemanticLab: https://github.com/OpenSemanticLab/mediawiki-extensions-OpenSemanticLab

Item
Type(s)/Category(s) Tutorial
CreativeWork
Article
Tutorial
Prerequisites (required)
Prerequisites (optional)
Follow-up (recommended) Transcend wikitext to store and edit structured & linked data
OSW Python Package
OSW Ontology Import
Item:OSW92619b0700984fe7913e5fbbd7f194dc

View as slide show

doi: https://doi.org/10.5281/zenodo.8086418

github: https://github.com/OpenSemanticLab

Background

KIproBatt Project Usecase


Motivation

OpenSemanticWorld Concept

Note: Read Transcend wikitext to store and edit structured & linked data first if you are already familiar with mediawiki

Link to related technologies

see also https://github.com/OO-LD/schema

Technology Stack

Why (Semantic) MediaWiki?

What is different to Vanilla (Semantic) MediaWiki?

  • All titles are UUIDs to make same global unique and stable + to allow changing the labels easily
    • Requires custom search interface (based on Skin:Citizen)
    • Requires replacements for some VE tools (Links, Media, Gallery)
  • Additional tools to place custom and inline editable content
  • Categories become nestable dataschemas (accessable for third-party tools) and render templates for their instances (also supporting metaschemas)
  • All structured data lives in the jsondata slot, templates are only used for rendering
  • All semantic annotations are done per json-ld context, which can be processed by many third-party tools without any SMW knowledge

Link to Ontologies

  • Ontology classes are just specific schemas / schemas represent "icons" of "real objects"
  • Automatic import via python (see: Python Examples)
  • Combination with data schemas, e. g. to populate autocomplete fields
  • Not just a static documentation: Planned community interaction features
    • Comments => Github Issues,
    • Edits => Re-export => Github Pull request


Usecases and Applications

  • Schema / Dataspace Catalogue
  • Ontologies Documentation, Visualization, Demonstration (s. a. Onto-Wiki)
  • Driver (=> CAMELS) , Tool and Workflow Registry (Early Demo)
  • Easy access to data science workflows: Onterface
  • LIMS, ELN (Demo), etc. in connection with ELN Standard Format, NOMAD, etc.
  • ... whatever you want it to be
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description
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