The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index; none of the 32 sources its claims cite is checked for support yet.
Organizing Scholarly Knowledge in the Open Research Knowledge Graph: An Open-Science Platform for FAIR Scholarly Knowledge
Open accessAbstract
The Open Research Knowledge Graph (ORKG) is an Open Science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge. Built on top of the RDF data model and extensible ontologies, the ORKG provides a common vocabulary for researchers to describe their research contributions and data, improving the discoverability and reusability of scholarly knowledge and research data. The ORKG includes tools for visualizing the relationships between different entities, making it easier to understand the connections between different pieces of research and their findings. It facilitates collaboration between researchers by providing a collaborative platform for organizing and sharing scholarly knowledge and data, reducing duplication and enabling more efficient use of resources. As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The Open Research Knowledge Graph (ORKG) is an open-science digital infrastructure built on RDF and extensible ontologies for producing, curating, publishing, and reusing machine-actionable scholarly knowledge. It structures research contributions into comparisons, visualizations, reviews, and observatories to improve discoverability, transparency, collaboration, and reuse.
Key findings
- The ORKG describes research contributions semantically and provides comparisons/overviews of contributions addressing the same research challenge, plus visualizations, literature lists, and review articles.
- It is built on the Resource Description Framework (RDF) data model, supports crowd-sourced curation, allows new properties/resources to be created on the fly, and uses pre-defined templates and NLP-assisted extraction.
- An example ORKG comparison covers 25 studies on scenarios for the transition to renewable energy in Germany, available at https://orkg.org/comparison/R153801/.
- The paper claims potential benefits including improved discoverability, increased reproducibility and transparency, facilitated collaboration, and reduced data silos; it also notes the ORKG team comprises more than 50 people worldwide.
Methods
- This is a descriptive/position essay about the ORKG platform, not an empirical study.
- It explains the platform’s design: RDF data model, extensible ontologies, crowd-sourcing, templates, NLP-assisted extraction, and a user-friendly interface for creating, editing, and searching research contributions.
- The only concrete example reported is a comparison of 25 studies on renewable energy transition scenarios in Germany; no sample, dataset, or statistical analysis is described.
Limitations
- The paper states no limitations.
- Evident from the text: the benefits are presented as potential rather than empirically evaluated, and no user study, performance metrics, or formal evaluation of the ORKG is reported.
- Evident from the text: the contribution is a short conceptual overview, so claims about improved discoverability, reproducibility, collaboration, and reduced duplication are not tested here.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 32 cited sources checked · 0 supported · 0 flagged
- Premisereasoning
Based on structured semantic descriptions, the ORKG provides comparisons (overviews) on research contributions addressing the same research challenge.
“Based on these structured and semantic descriptions, the ORKG provides comparisons (i.e.overviews) on research contributions addressing the same research challenge.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The structured representation of findings and scenarios from these studies enables novel ways of analysis, e.g., about the installed capacity of renewable energy sources.
“The structured representation of the findings and scenarios from these studies enables novel ways of analysis e.g. about the installed capacity of renewable energy sources.”
Quote in the paperSubstantial claim - Interpretationreasoning
The ORKG can improve the reproducibility of research, as other researchers can more easily replicate experiments and verify results.
“This can improve the reproducibility of research, as other researchers can more easily replicate experiments and verify results.”
Quote in the paperSubstantial claim - Premisecitation
The ORKG provides a user-friendly interface that allows researchers to create, edit, and search for research contributions.
“The ORKG provides a user-friendly interface that allows researchers to create, edit, and search for research contributions.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisereasoning
The ORKG provides a common vocabulary for researchers to describe their research contributions and data.
“The emerging domain ontologies provide a common vocabulary for researchers to describe their data, making it easier to find and understand.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisereasoning
Contributors can add, curate, and organize descriptions of scientific contributions in a crowd-sourcing manner.
“Based on this data model contributors can add, curate, and organize descriptions of scientific contributions in a crowd-sourcing manner.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The ORKG facilitates collaboration between researchers by providing a collaborative platform for organizing and sharing scholarly knowledge and data.
“Thirdly, the ORKG can facilitate collaboration between researchers.”
Quote in the paperSubstantial claim - Interpretationreasoning
The ORKG can improve the discoverability of research.
“Firstly, it can improve the discoverability of research.”
Quote in the paperSubstantial claim - Premisecitation
Automated extraction methods leveraging Natural Language Processing assist users in creating structured representations.
“Automated extraction methods leveraging Natural Language Processing assist users in creating structured representations.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premiseown data
The ORKG includes tools for visualizing the relationships between different entities.
“The platform also includes tools for visualizing the relationships between different entities, making it easier to understand the connections between different pieces of research.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The ORKG can help to address the issue of data silos in research.
“Finally, the ORKG can help to address the issue of data silos in research.”
Quote in the paperSubstantial claim- Rests on an assumption: Many researchers collect and analyze data in isolation, leading to duplicate efforts and wasted resources.
- Interpretationreasoning
The ORKG can save time and effort, as researchers no longer need to sift through large amounts of unstructured data.
“This can save time and effort, as researchers no longer need to sift through large amounts of unstructured data.”
Quote in the paperSubstantial claim
- Probably not a substantial claim. 18
- It may state two separate claims as one. 11
- The quote only partly states this claim. 2
Probably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataAn ORKG comparison of 25 studies regarding scenarios for the transition to renewable energy in Germany is available online.
“An example of an ORKG comparison of 25 studies regarding scenarios for the transition to renewable energy in Germany is shown in Figure 2 and available online at https://orkg.org/comparison/R153801/.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper may give no reason why it holds.
InterpretationreasoningBy providing a shared platform for organizing and sharing research conceptualizations and data, the ORKG can enable researchers to work together more effectively (e.g., simplifying the creation of meta-analyses).
“By providing a shared platform for organizing and sharing research conceptualizations and data, the ORKG can enable researchers to work together more effectively (e.g.simplifying the creation of meta-analyses).”
Quote in the paperNot a substantial claimProbably not a substantial claim.
Premisenone givenThe ORKG is an open-science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge.
“The Open Research Knowledge Graph (ORKG) is an Open Science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
PremisecitationThe ORKG is built on the RDF data model and extensible ontologies.
“Built on top of the RDF data model and extensible ontologies, the ORKG provides a common vocabulary for researchers to describe their research contributions and data, improving the discoverability and reusability of scholarly knowledge and research data.”
- Also stated in: § 3 How does the ORKG work? · plain
“The ORKG is built on top of the Resource Description Framework (RDF) data model.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Also stated in: § 3 How does the ORKG work? · plain
Probably not a substantial claim. It may state two separate claims as one.
InterpretationreasoningThe ORKG provides a common platform for researchers to share their contributions, data, and methods, thus reducing duplication and enabling more efficient use of resources.
“The ORKG provides a common platform for researchers to share their con-tributions, data, and methods, thus reducing duplication and enabling more efficient use of resources.”
Quote in the paperSubstantial claim- Rests on an assumption: Many researchers collect and analyze data in isolation, leading to duplicate efforts and wasted resources.
Probably not a substantial claim. The paper may give no reason why it holds.
InterpretationreasoningThe ORKG's visualization tools make it easier to understand the connections between different pieces of research and their findings.
“making it easier to understand the connections between different pieces of research and their findings.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
InterpretationreasoningThe ORKG enables researchers to build on each other's work and collaborate more effectively.
“enabling researchers to build on each other's work and collaborate more effectively.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisereasoningBased on structured descriptions, the ORKG provides other features including visualizations, literature lists, and review articles.
“Based on the structured descriptions of research contributions a number of other features are provided.This includes visualizations, literature lists, and review articles comprising the structured content.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationPre-defined templates allow the structuring of typical, reusable knowledge patterns.
“Pre-defined templates allow the structuring of typical, reusable knowledge patterns.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
InterpretationreasoningBy organizing research contributions into a structured format and providing a common vocabulary, the ORKG makes it easier for researchers to obtain comparative overviews of research approaches addressing a common research problem and to find relevant data.
“By organizing research contributions into a structured format and providing a common vocabulary, the ORKG makes it easier for researchers to obtain comparative overviews of research approaches addressing a common research problem and to find relevant data for their work.”
Quote in the paperNot a substantial claimProbably not a substantial claim.
PremisecitationThe ORKG is a knowledge graph that connects scientific research papers, datasets, and methods.
“The ORKG is a knowledge graph that connects scientific research papers, datasets, and methods.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one. The quote only partly states this claim.
Interpretationnone givenThe ORKG has several potential benefits for researchers, including improved discoverability, increased transparency, facilitated collaboration, and reduced data silos.
“The ORKG has several potential benefits for research data enthusiasts, including improved discoverability, increased transparency, facilitated collaboration, and reduced data silos.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
PremisereasoningNew properties and resources for describing papers, datasets, and methods can be created on the fly, enabling a domain-specific and evolving ontological description of the relationships between different research entities.
“New properties and resources for describing papers, datasets, and methods can be created on the fly, thus enabling a domain-specific and evolving ontological description of the relationships between different research entities.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
InterpretationreasoningThe ORKG reduces duplication and enables more efficient use of resources.
“reducing duplication and enabling more efficient use of resources.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
PremisecitationThe ORKG is designed to be modular and extensible, allowing researchers to add and link to their own ontologies and customize the platform to suit their specific research needs.
“The ORKG is also designed to be modular and extensible, allowing researchers to add and link to their own ontologies and customize the platform to suit their specific research needs.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper may give no reason why it holds.
InterpretationreasoningBy providing a clear description of the relationships between different entities and artifacts, the ORKG makes it easier for researchers to understand how data was generated and how it can be used.
“By providing a clear description of the relationships between different entities and artifacts, the ORKG makes it easier for researchers to understand how data was generated and how it can be used.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one. The quote only partly states this claim.
InterpretationreasoningThe ORKG improves the discoverability and reusability of scholarly knowledge and research data.
“The ORKG is designed to improve the discoverability and reusability of research data, enabling researchers to build on each other's work and collaborate more effectively.”
Quote in the paperNot a substantial claimProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
InterpretationreasoningAs research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.
“As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.”
Quote in the paperNot a substantial claim- Rests on an assumption: Research is becoming increasingly data-driven.
Probably not a substantial claim.
PremisecitationThe ORKG provides a structured way of organizing and sharing research knowledge and data.
“It is an open-science platform that provides a structured way of organizing and sharing research knowledge and data.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
InterpretationreasoningThe ORKG can increase the reproducibility and transparency of research.
“Secondly, the ORKG can increase the reproducibility and transparency of research.”
Quote in the paperSubstantial claimProbably not a substantial claim.
PremisecitationThe core feature of the ORKG is to describe research contributions in a semantic manner.
“The core feature of the ORKG is to describe research contributions (e.g.published in an article) in a semantic manner.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
InterpretationreasoningThe ORKG can lead to new insights and discoveries that would not have been possible otherwise.
“This can lead to new insights and discoveries that would not have been possible otherwise.”
Quote in the paperNot a substantial claimProbably not a substantial claim.
PremisecitationThe ORKG allows researchers to share and access research knowledge and data in a more efficient, transparent, and collaborative way.
“The Open Research Knowledge Graph (ORKG) is a novel open-science platform that allows researchers to share and access research knowledge and data in a more efficient, transparent, and collaborative way [1], [2].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
The paper relies on this without support.
Scholarly communication is still based on static and relatively unstructured documents.
“While many domains have significantly transformed in the digital age (e.g.encyclopedias, mail-order catalogs, street maps) scholarly communication is still based on static and relatively unstructured documents.”
The paper relies on this without support.
Static and unstructured scholarly communication results in severe problems, such as a publication flood, deterioration of peer review, and inadequacy of machine assistance.
“This results in severe problems, such as a publication flood, deterioration of peer review, and inadequacy of machine assistance.”
The paper relies on this without support.
Many researchers collect and analyze data in isolation, leading to duplicate efforts and wasted resources.
“Many researchers collect and analyze data in isolation, leading to duplicate efforts and wasted resources.”
The paper relies on this without support.
Research is becoming increasingly data-driven.
“As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.”
Citations
Cites
Improving Access to Scientific Literature with Knowledge Graphs
Auer, S., Oelen, A., Haris, M., Stocker, M., D’Souza, J., Farfar, K. E., Vogt, L., Prinz, M., Wiens, V., & Jaradeh, M. Y. (2020) · Bibliothek Forschung und Praxis · doi:10.1515/bfp-2020-2042 · Cited by 82
Self-citationFAIR scientific information with the Open Research Knowledge Graph
Stocker, M., Oelen, A., Jaradeh, M. Y., Haris, M., Oghli, O. A., Heidari, G., Hussein, H., Lorenz, A.-L., Kabongo, S., Farfar, K. E., et al. (2023) · Fair Connect · doi:10.3233/fc-221513 · Cited by 63
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Organizing Scientific Knowledge From Energy System Research Using the Open Research Knowledge GraphPreprint
Karras, O., Göpfert, J., Kuckertz, P., Pelser, T., & Auer, S. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.13365 · Cited by 5
Self-citationKarras, O. (2025) · Information - Wissenschaft & Praxis · doi:10.1515/iwp-2024-2052 · Cited by 1
Self-citationThe Future of AI-Powered Research Transforming Libraries Into Intelligent Ecosystems
Khoso, R. H., Manzoor, A., & Alam, S. H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7262-4.ch016
Metadata
- Authors
- Auer, S., Stocker, M., Karras, O., Oelen, A., D'Souza, J., & Lorenz, A.-L.
- Affiliations
- Technische Informationsbibliothek (TIB) (DE)
- Venue
- Proceedings of the Conference on Research Data Infrastructure
- Publisher
- TIB Open Publishing
- Published
- 2023-09-07
- Volume
- 1
- ISSN
- 2941-296X (ISSN-L)
- OpenAlex
- W4386546557
- Cited by
- 3
- References
- 2
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1
- FWCI
- 0.34
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the Conference on Research Data Infrastructure (publishedVersion, cc-by)
- Funders
- European Research Council (819536)
- Deutsche Forschungsgemeinschaft (442146713, 460234259, 501865131)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Computer Science › Information Systems › Library Science and Information Systems
- Keywords
- Open Research Knowledge Graph, knowledge graphs, RDF, ontology (information science), open science, research data infrastructure, research data curation, research data reuse, research collaboration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
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