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==Use Cases==
==Use Cases==
*Searching identifiers of German companies for the period 1956-2018
*Searching identifiers of German companies for the period 1956-2018
*Matching your (meta)data to AKF data using the [https://akf.mbi.kgi.uni-mannheim.de AKF reconciliation service]
*Matching your (meta)data to AKF data using the [https://rec.akf.kgi.uni-mannheim.de AKF reconciliation service]
*Clean your tabular data and match it to the AKF knowledge graph with [https://openrefine.org OpenRefine]
*Clean your tabular data and match it to the AKF knowledge graph with [https://openrefine.org OpenRefine] and the [https://rec.akf.kgi.uni-mannheim.de AKF reconciliation service]
*Semantic annotation of tabular data using the AKF knowledge graph with [https://github.com/UB-Mannheim/bbw bbw]
*Semantic annotation of tabular data using the AKF knowledge graph with [https://github.com/UB-Mannheim/bbw bbw]
*Federated queries over AKF, Wikidata and other SPARQL endpoints
*Federated queries over AKF, Wikidata and other SPARQL endpoints

Latest revision as of 11:53, 11 May 2023

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Overview

The Aktienführer (AKF) Knowledge Graph is free and open. It contains structured (meta)data for the German listed stock companies from the Hoppenstedt-Aktienführer from 1956 to 2018. The metadata includes official company names with time ranges, company identifiers (ISIN and WKN) and Aktienführer IDs.

We enriched metadata via entity linking with other databases: Creditreform, Bureau van Dijk Orbis, GND and Wikidata. The knowledge graph contains 342 external identifiers representing various databases, including the valuable company databases: Crefo number, BvD ID, Orbis ID, Handelsregisternummer, PM20 folder ID, European VAT number, D&B Hoovers company profile, Bloomberg company ID, ROR ID, Legal Entity Identifier (LEI), Google Knowledge Graph ID, PermID, OpenSanctions ID, LinkedIn organization ID, German Lobbyregister ID, North Data ID and OpenCorporates ID.

Access to (meta)data

Use Cases

  • Searching identifiers of German companies for the period 1956-2018
  • Matching your (meta)data to AKF data using the AKF reconciliation service
  • Clean your tabular data and match it to the AKF knowledge graph with OpenRefine and the AKF reconciliation service
  • Semantic annotation of tabular data using the AKF knowledge graph with bbw
  • Federated queries over AKF, Wikidata and other SPARQL endpoints

Get involved

External Resources

Acknowledgments

This work was funded by the Ministry of Science, Research and Arts of Baden-Württemberg through the project “BERD@BW" and by the German Research Foundation (DFG) through the project "BERD@NFDI".