Open Research Knowledge Graph:A System Walkthrough
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Abstract (arXiv)
Despite improved digital access to scholarly literature in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. Scholarly knowledge remains locked in representations that are inadequate for machine processing. The Open Research Knowledge Graph (ORKG) is an infrastructure for representing, curating and exploring scholarly knowledge in a machine actionable manner. We demonstrate the core functionality of ORKG for representing research contributions published in scholarly articles. A video of the demonstration and the system are available online.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2022.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 2.
5 papers.
- 2024Contri(e)ve: Context + Retrieve for Scholarly Question Answeringcited by 2
- 2024Beyond the Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboardscited by 2
- 2025Open-Source Agentic Hybrid RAG Framework for Scientific Literature Reviewcited by 2
- 2026paper.json: A Coordination Convention for LLM-Agent-Actionable Paperscited by 0
- 2026BizSage: A Self-Evolving Multi-Agent Framework for Business Research with Efficient Knowledge Retrievalcited by 0
- 2026BizSage: A Self-Evolving Multi-Agent Framework for Business Research with Efficient Knowledge Retrieval3 Sep 2026
- 2026paper.json: A Coordination Convention for LLM-Agent-Actionable Papers15 May 2026
- 2025Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review30 Jul 2025
- 2024Beyond the Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboards7 Nov 2024
- 2024Contri(e)ve: Context + Retrieve for Scholarly Question Answering13 Sep 2024