Description
This session introduces a powerful workflow for bridging the gap between local data and the global Linked Open Data (LOD) ecosystem. Participants learn how to use OpenRefine, an open-source data manipulation tool, to "reconcile" their data by comparing local strings (like author names or subject headings) against Wikidata, the massive multilingual knowledge base that powers Wikipedia. We move beyond basic data cleaning to explore how to fetch live data from web APIs and how to contribute information back to the global community. By the end of the session, you understand how to turn a flat spreadsheet into a rich, interconnected dataset ready for the Semantic Web.
Prerequisites
Data Cleaning with OpenRefine or a basic familiarity with OpenRefine (importing data, faceting, and filtering).
Participants should be comfortable using a computer and have a general understanding of spreadsheets. No prior programming experience is necessary.
Details
Learning Objectives
After completing the workshop, learners will be able to:
- Discuss what Linked Open Data is and why we use it
- Connect OpenRefine to the Wikidata Reconciliation Service
- Reconcile data against Wikidata to match and disambiguate entities.
- Retrieve data from external APIs (such as CrossRef or VIAF).
- Write basic GREL (General Refine Expression Language) to parse structured data (JSON/HTML) returned from web services.
- Enrich datasets with properties pulled from Wikidata
- Contribute cleaned data back to Wikidata through OpenRefine's built-in upload workflow
Who Should Enrol
Researchers or librarians who work with “messy” data: large volumes of bibliographic or metadata records, inconsistent naming conventions, or the need to standardize data.