Knowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains

Betancort, Noemi

Bölling, Christian

Dietze, Stefan

Koepler, Oliver

Murphy, Bridget

Rossenova, Lozana

Schubotz, Moritz

Shigapov, Renat

Stocker, Markus

MAnnheim DOCument Server

Name of the working groupKnowledge GraphsAcronymsection-metadata-wg-kgContact personsRenat Shigapov (Universitätsbibliothek Mannheim, BERD@NFDI),renat.shigapov@uni-mannheim.deLozana Rossenova (TIB – Leibniz-Informationszentrum Technik undNaturwissenschaften und Universitätsbibliothek, NFDI4Culture),lozana.rossenova@tib.euMoritz Schubotz (FIZ Karlsruhe - Leibniz Institute for Information Infrastructure,MaRDI), moritz.schubotz@fiz-karlsruhe.deAuthorsMarkus Stocker (0000-0001-5492-3212), Lozana Rossenova(0000-0002-5190-1867), Renat Shigapov (0000-0002-0331-2558), Noemi Betancort(0000-0002-0156-3556), Stefan Dietze,  Bridget Murphy (0000-0002-1354-2381),Christian Bölling (0000-0002-6544-1363), Moritz Schubotz (0000-0001-7141-4997),Oliver Koepler (0000-0003-3385-4232)Version 1.1DOI: 10.5281/zenodo.75153242023-01-09AbstractKnowledge Graphs are a key technology for implementing the FAIR principles in datainfrastructures by ensuring interoperability for both humans and machines. The WorkingGroup "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of theGerman National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur(NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitatecross-domain data interlinking and federation following the FAIR principles, and to contributeto the joint development of tools and technologies that enable transformation of structuredand unstructured data into semantically reusable knowledge across different domains.1. MotivationKnowledge Graphs (KG) are a widely used database technology for the flexible descriptionof entities and their linking enabling advanced information modeling, exploration, and use.Since the establishment of the Google Knowledge Graph1, these technologies have gainedmomentum in industry and in research, where KGs are used to organize and publishinformation about both physical entities and information artifacts, like articles, datasets,software, samples, instruments, contributors, organizations and their interrelations in amachine-interpretable way. OpenAIRE Research Graph2, GESIS Knowledge GraphInfrastructure3, TIB Open Research Knowledge Graph4, Springer Nature SciGraph5,Research Graph Foundation6, the GND.network7, and PID Graph8 are establishedinfrastructures which connect research data and research-related entities and make itaccessible. Besides this, the Wikidata knowledge graph9 contains a large number ofresearch-related entities10 and several domain specific Wikibase11 instances are used byvarious research communities12.Individual NFDI consortia develop services for the discovery of their assets, e.g., datasets,services, organizations, etc. For the research community served by a particular NFDIconsortium, these services act as the primary entry point. However, given the increasingly12 https://portal.mardi4nfdi.de/wiki/Links#Other_wikibase_instances11 https://wikiba.se10 https://scholia.toolforge.org9 https://www.wikidata.org8 https://commons.datacite.org7 https://gnd.network6 https://researchgraph.org5 https://www.springernature.com/gp/researchers/scigraph4 https://orkg.org3 https://www.gesis.org/forschung/angewandte-informatik/knowledge-graph-infrastruktur2 https://graph.openaire.eu1 https://blog.google/products/search/introducing-knowledge-graph-things-notinterdisciplinary use of resources (e.g., datasets, methods or models), FAIR metadata aboutresources across individual disciplines and their dependencies is crucial and lays thefoundation for multi- and cross-disciplinary discovery. This is even more important given thewidespread use of computational methods (e.g., NLP or ML models) across variousdisciplines and their dependencies on data resources. This leads to a genuine need forFAIR13 and machine-interpretable representations of resources and their dependenciesacross NFDI. Some NFDI consortia already develop the knowledge graphs to support theirdata infrastructures, e.g., NFDI4Culture KG14 and MaRDI Wikibase KG15.KGs are a key technology for the implementation of the FAIR Principles by ensuringinteroperability for both machine and human access through the use of a joint data model,adoption of established and interlinked schemas and vocabularies, and reliance onconsistent Persistent Identifiers (PIDs)16 and data management policies across the entiredata space.The motivation of this working group is two-fold:1. To promote the use of Knowledge Graphs in all NFDI consortia and facilitatecross-domain data interlinking and federation following the FAIR Principles; and2. To contribute to the joint development of tools and technologies that enabletransformation of structured and unstructured data into semantically reusableknowledge across different domains represented by NFDI consortia.2. Objectives● Enable transdisciplinary connection between research data assets from differentconsortia via an overarching KG approach.● Assessment of the current state of KG technology implementation across thedifferent NFDIs and identification of possible factors blocking wider adoption of KGswithin individual consortia.● Agree on an approach to support a single point of access to federated informationabout research assets in the NFDI, across all NFDI initiatives.16 We acknowledge that the term ‘PID’ is currently used to reference a wide range of identifiers and approachesto identification in semantic contexts. We will work with other relevant NFDI WGs to come to a commonunderstanding of the term. Furthermore, the work of relevant external communities such as the PID IG of theRDA will also be taken into consideration.15 https://portal.mardi4nfdi.de/wiki/Portal14 https://nfdi4culture.de/resources/knowledge-graph.html13 Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship.In Scientific Data (Vol. 3, Issue 1). Springer Science and Business Media LLC.https://doi.org/10.1038/sdata.2016.18● Collaborate closely with the WG Ontology Mapping and Harmonization17 to establishwhat common identifiers and connecting properties are required in order to facilitateunified access to heterogeneous resources from across the different consortiadomains.● Demonstrate how individual consortia can make their KG data (and other structureddata resources) interoperable with other consortia (via federated access) in practice(cf.  WG Cookbook(s), Guidance and Best Practices18).● Reach out and collaborate with open source communities developing andmaintaining KG technologies to ensure that particular requirements of the NFDIresearch infrastructure are taken into consideration and necessary steps takentowards implementing additional software customizations and extensions asrequired.● Define the core requirements for a flexible and easy-to-use entry point solution foradding data to the overall knowledge graph.3. Work PlanQ4 2022: Evaluating the state of the art● Define criteria and assess the current state of KG technology adoption within theNFDI consortia.● Evaluate the use of existing integration and federation approaches (e.g., the PIDGraph and its GraphQL-based implementation by DataCite19, or cross linking viaexternal identifiers in Wikidata).Q4 2022 - Q4 2023: Communication activities● Provide regular updates about WG activities.● Knowledge sharing in NFDI working groups and consortia, and relevant externalworking groups.20● Provide platforms for collaboration with KG tool maintainer groups (e.g. open sourcecommunities around Wikidata, Wikibase, among others) and other stakeholders.● Prepare for and participate in NFDI Conference 2023 and Section Workshops.Q1 2023: Overviews● Publish an overview of KG implementations in NFDI consortia and their respectivedomains.20 See the section: “Connected to other working groups outside NFDI” further in this document.19 This is an outcome of the FREYA H2020 Project.18 See WG Charter: https://zenodo.org/record/675825617 See WG Charter: https://zenodo.org/record/6726519● Identify best practices and/or factors blocking the implementation of KG technologyon the individual consortia-level, so that lessons learned can inform the cross-domainwork as well.● Define potential NFDI Base Services21 based on the results of this assessment andevaluation work.Q2 - Q3 2023: Implementation, piloting and initializing the feedback process● Coordinate closely with the WG Ontology Mapping and Harmonization as well asNFDI Base Services teams in the implementation of a pilot cross-domain KG accesspoint. This will involve any necessary data integration and/or schema linking activitiesnot covered by other working groups.● Coordinate closely with the WG Terminology Services22 as well as Base4NFDIconsortium and its respective task areas teams in the further development andimprovement of KG-supporting infrastructure (including tools for LOD modeling andstorage, terminology mapping, and application profile definition) in use by individualconsortia.● Coordinate testing sessions and feedback gathering with concrete cross-domain usecases for KG services.Q3 - Q4 2023: Dissemination and documentation of  outcomes● Publication of a white paper summarizing the WG activities.● Talks, tutorials and workshops to working groups, NFDI consortia, and other groupsof interests.Specific tasks● Initiate regular bi-monthly and later monthly meetings for the working group.● Evaluate existing KG tools and technologies in use within the NFDI consortia (andbeyond).● Assessment of existing approaches to achieve transdisciplinary data discovery, e.g.,GraphQL.● Identify and prioritize concrete needs for cross-domain KG access, by sub-workinggroups involving all consortia.● Provide recommendations to individual consortia on the use of specific KGinfrastructure setups, as well as recommendations for the overarching integrated KGapproach.22 See WG Charter: https://zenodo.org/record/675932521 See joint statement on basic services: https://zenodo.org/record/6091657#.YgvB8t8xk2w● Provide example implementations of both domain-specific and cross-domain KGinfrastructure setups.● Collaborate with other relevant WGs to deliver the identifier mappings and commonproperties that can facilitate integrated access to a single discovery endpoint for allNFDIs.4. Initial Membership ListName Institution NFDI consortia1 Lozana Rossenova TIB NFDI4Culture2 Markus Stocker TIB NFDI4DataScience,NFDI4Earth3 Renat Shigapov UB Mannheim BERD@NFDI4 Noemi Betancort Uni Bremen KonsortSWD5 Felix Engel TIB NFDI4Ing6 Moritz Schubotz FIZ MaRDI7 Rebecca Wilm IDS Text+8 Heinrich Widmann DKRZ NFDI4Earth9 Claus Weiland Senckenberg NFDI4Earth,FAIRagro10 Katrin Moeller MLUHalle-WittenbergNFDI4Memory11 Sonja Schimmler Fraunhofer FOKUS NFDI4DataScience,NFDI4Cat12 Jakob Voß VZG NFDI4Objects13 Ziyad ZB MED NFDI4Microbiota14 Jürgen Kett DNB Text+,NFDI4Culture15 Konrad Förstner ZB MED NFDI4Microbiota16 Stefan Dietze GESIS, HHU NFDI4DataScience,BERD@NFDI17 Cord Wiljes NFDI-GS18 Marie Annisius DNB Text+19 Bridget Murphy CAU DAPHNE4NFDI20 Sven Nahnsen EKUT GHGADataPlant21 Christian Bölling MfN Berlin NFDI4Biodiversity22 Gerhard Heyer SAW Leipzig Text+23 Thomas Eckart SAW Leipzig Text+24 Daniel Mietchen FIZ MaRDI25 Benjamin Zapilko GESIS NFDI4DataScience5. Adoption PlanOnce assessment of the current state of KG adoption across the consortia is complete andconcrete requirements for a cross-domain KG access point have been specified, feedback tothe consortia and the scientific communities they represent is going to be crucial for thesuccess of the WG.Given the NFDI’s bottom-up approach, the Section Metadata and the WG KG are the forumsto discuss the best KG practices and to exchange knowledge about KGs across all NFDIdomains. The WG KG is going to support the consortia in deciding which KG services andrelated data integration strategies should be used and how they should be modified andadopted. Furthermore, the WG will provide the expertise required in specifying the NFDIBase Services concerning KGs, and subsequently provide support to the teams developingthe basic services.Adoption plans for individual cases of KG implementation (including the cross-domainaccess point) may differ, depending on the state of the art within each domain. They aregoing to be agreed on a case-by-case basis within each consortium and with guidance fromthis WG as required.Represented ConsortiaNFDI4CultureBERD@NFDINFDI4DataScienceNFDI4EarthKonsortSWDNFDI4IngMaRDIText+NFDI4ChemNFDI4MemoryNFDI4CatNFDI4ObjectsNFDI4MicrobiotaDAPHNE4NFDIGHGADataPlantNFDI4BiodiversityConnected to other NFDI working groupsWG Ontology Mapping and HarmonizationWG Semantic Interoperability: Terminology ServicesWG Cookbooks, Guidance, Best PracticesWG “Training Infrastructures” of Section “Training & Education”WG “Data Integration” of Section “Common Infrastructures”NFDI@WikidataConnected to other working groups outside NFDIOpen Research Knowledge GraphWikimedia Germany (Wikidata)Wikibase Stakeholders GroupOpen Science Graphs for FAIR Data IG (RDA)

Knowledge graphs - working group charter (NFDI section-metadata)

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