KATI® Lab

The increasing availability of large and diverse datasets presents both scientists and decision-makers with the challenge of systematically generating information and actionable knowledge. For foresight, innovation and decision-making processes in particular, it is essential to efficiently capture relevant data, analyze it from multiple perspectives and derive well-founded conclusions. Data Driven Foresight addresses exactly this: data becomes a strategic tool that enables early orientation in dynamic and complex future fields.

For several years, the research department INT has been exploring how foresight processes can be supported and enhanced through the targeted use of IT- and data-based methods. To make this approach practically usable, the KATI^®-system – Knowledge Analytics for Technology & Innovation – was developed. KATI^® provides data-driven analyses for foresight processes and makes them accessible to users. At its core, KATI^® is a powerful tool for literature and patent data analyses that go well beyond traditional search functions and offers innovative analytical possibilities.

The aim of KATI^® is to capture the current state of the art of any research topic, identify its origins and highlight potential development paths. In addition, relevant actors and their networks within a research field are analyzed. This type of multi-perspective analysis, enabled by KATI^®, is a central component for successful and efficient technology foresight and data-driven decision-making. The system is aimed at anyone working in foresight, technology and innovation management or generally speaking science intelligence.

KATI®'s approach to data driven foresight is based on three central components that enable research-oriented and systematic analysis of scientific data:

1. Data as a foundation: The collection, preparation, maintenance and provision of relevant datasets form the basis for all further analyses. KATI® processes metadata from scientific publications and patents and integrates it into a powerful graph database that maps complex relationships between information objects, such as publications, authors, institutions or countries. If needed, these analyses can be enriched with additional data to, for example, capture economic aspects. 
2. Analyses for knowledge generation: Methods from statistics, machine learning, data mining, natural language processing and AI algorithms are used for the analysis and interpretation of data, implemented and further developed in the KATI® Lab. These methods are combined with customized visualizations. By applying these techniques, signals, patterns, trends and knowledge trajectories are identified, which are of central importance for technology foresight and innovation analysis.
3. Use cases as an application framework: Specific questions are derived from foresight and innovation processes and addressed using data-driven approaches. The advanced analytics component of KATI® enables the flexible integration of additional use-case-specific data sources and the execution of targeted analyses.

The continuous expansion and updating of the data foundation ensure that KATI® remains up to date. The combination of a high-performance search engine, an intuitive user interface and a modular analysis system makes KATI® a powerful tool for data-driven, forward-looking decision-making in research and development.

KATI4Fraunhofer 

Martini, M., & John, M. (2025). Technology Foresight from a Use-Case Perspective: A Comprehensive Framework for Data-Driven Patent Analysis. In I. Bitran (Chair), International Society for Professional Innovation Management (ISPIM Conference) 2025. Symposium conducted at the meeting of ISPIM, Bergen, Norway.

Scheuffele, M., Martini, M., John, M., & Brecht, L. (2025). Job Postings Analysis as a Tool for Technology Foresight. In I. Bitran (Chair), International Society for Professional Innovation Management (ISPIM Conference) 2025. Symposium conducted at the meeting of ISPIM, Bergen, Norway.

Martini, M., & John, M. (2024). Building a framework for patent analysis in data driven foresight - A use case focused conclusion. In Nordic Workshop on Bibliometrics & Science Policy. Symposium conducted at the meeting of University of Iceland, Reykjavík, Iceland.

Sarin, P. (2024). Natural Language Processing and Topic Modeling for Exploring Trends in Human-Robot Interaction [Masterthesis, Hochschule Bonn-Rhein-Sieg, Sankt Augustin]

Martini, M., Tietze, F., John, M., Aristodemou, L., Schönmann, A., & Schimpf, S. (2024). Conceptualizing disruptive innovation paths, patent zero and patent-data based operationalization. In XXXV ISPIM Innovation Conference: Local Innovation Ecosystems for Global Impact. Symposium conducted at the meeting of International Society for Professional Innovation Management (ISPIM), Tallinn, Estonia.

Schropp, T. C., Martini, M., Kaiser, S., & John, M. (2024). Cognitive Biases in Data-Driven Decision-Making - A Literature Review. In XXXV ISPIM Innovation Conference: Local Innovation Ecosystems for Global Impact. Symposium conducted at the meeting of International Society for Professional Innovation Management (ISPIM), Tallinn, Estonia.

Baaden, P., Rennings, M., John, M., & Bröring, S. (2024). On the emergence of interdisciplinary scientific fields: (how) does it relate to science convergence? Research Policy, 53(6), 105026. https://doi.org/10.1016/j.respol.2024.105026

Rennings, M., Baaden, P., Block, C., John, M., & Bröring, S. (2024). Assessing emerging sustainability-oriented technologies: the case of precision agriculture. Scientometrics, 129(6), 2969–2998. https://doi.org/10.1007/s11192-024-05022-2

Dadashi, A., Schönmann, A., Martini, M., & John, M. (2024). Technologie-Reifegradbewertung im Gesundheitswesen – Eine bibliometrische Analyse. In M. A. Pfannstiel (Ed.), Technologien und Technologiemanagement im Gesundheitswesen: Potenziale nutzen, Lösungen entwickeln, Ziele erreichen (1st ed.). Springer Gabler. https://doi.org/10.1007/978-3-658-43860-9_12

Ellermann, K., Martini, M., & John, M. (2024). Use Cases for Artificial Intelligence in Technology Foresight: A Systematic Literature Review. In R&D Management 2024: Transforming industries through technology, Stockholm.
https://doi.org/10.24406/publica-3372

Martini, M., & John, M. (2023). Evolving Co-classifications – measuring technology maturity over time with patents. In Y. Y. Ahn, J. Bollen, K. Börner, K. W. Boyack, S. Fortunato, S. Milojević, . . . C. S. Wagner (Chairs), 19th International Conference on Scientometrics & Informetrics. Symposium conducted at the meeting of International Society for Scientometrics and Informetrics, Bloomington, USA.

Martini, M., & John, M. (2023). Structuring the unknown - insights on waste management research in Europe. In International Society for Professional Innovation Management (Chair), XXXIV ISPIM Innovation Conference "Innovation and Circular Economy" 2023. Symposium conducted at the meeting of International Society for Professional Innovation Management (ISPIM), Ljubljana, Slovenia.

Martini, M., & John, M. (2023). The use of patent analysis in foresight, insights and assessments of methods and approaches. In A. Friberg, J. Rahman, M. Schirone, C. Granell, & P. Arhanto (Chairs), 28th Nordic Workshop on Bibliometrics and Research Policy, Gothenburg, Sweden.

John, M., Schimpf, S., & Martini, M. (2023). Zukunft der Innovation – eine Spurensuche in den Daten. In R. Dumitrescu & K. Hölzle (Chairs), 17. Symposium für Vorausschau und Technologieplanung. Symposium conducted at the meeting of Heinz Nixdorf Institut, Universität Paderborn, Berlin.

Martini, M., & John, M. (2022). The use of patent analysis in foresight. A data-driven review. In Holmberg, Maleki et al. (Hg.) September 21-23, 2022 – Nordic Workshop on Bibliometrics. https://doi.org/10.24406/publica-762

Steinmüller, K., Burchardt, A., Gondlach, K., Gracht, H. von der, Kisgen, S., Ellermann, K., Martini, M., & John, M. (2022). Kann Künstliche Intelligenz Zukunftsforschung? ‐ Ein spekulativer Impuls. Zeitschrift Für Zukunftsforschung, 2022(1). https://doi.org/10.63370/zfz.v10i1.99

John, M., Becker, B., Fritsche, F., Gülden, C., Martini, M., & Scheid, S. (2022). Data Driven Foresight & KATI – Number Crunching für die Technologiefrühaufklärung. In M. Lauster, R. Bantes, & J. Kohlhoff (Eds.), Neue Technologien: Kernthemen des Technologiemonitorings am Fraunhofer INT zwischen 2009 und 2021 (p. 17). Fraunhofer Verlag.

John, M., Fritsche, F., & Gülden, C. (2021). Where to start reading? Introducing the reference-citation plot. In W. Glänzel, S. Heeffer, P.-S. Chi, & R. Rousseau (Eds.), Issi2021: 18th International Conference on Scientometrics & Informetrics (pp. 539–544). International Society for Scientometrics and Informetrics (I.S.S.I.); ISSI Society Centre for R&D Monitoring (ECOOM) KU Leuven.

John, M. (2018). Data driven foresight - Technologiefrühaufklärung im Zeitalter von Big and Linked Data. Ein Werkstattbericht. In J. Gausemeier, W. Bauer, & R. Dumitrescu (Chairs), 14. Symposium für Vorausschau und Technologieplanung. Symposium conducted at the meeting of Universität Paderborn; Heinz Nixdorf Institut, Berlin.

Bar-Ilan, J., John, M., Koopman, R., Wang, S., Mayr, P., Scharnhorst, A., & Wolfram, D. (2016). Bibliometrics and information retrieval: Creating knowledge through research synergies. Proceedings of the Association for Information Science and Technology, 53(1), 1–4. https://doi.org/10.1002/pra2.2016.14505301023

John, M., & Fritsche, F. (2013). Bibliometric classification of emerging topics. In S. Hinze & A. Lottmann (Eds.), iFQ-Working Paper, Translational twists and turns: Science as a socio-economic endeavor (pp. 181–184). iFQ.

John, M., & Fritsche, F. (2013). Bibliometrics for technology forecasting and assessment - a preliminary application to human enhancement. In L. Hebakova, C. Scherz, & L. Klüver (Chairs), 1st PACITA project conference. Symposium conducted at the meeting of Karlsruhe Institute of Technology and Systems Analysis and Technology Centre of the Academy of Sciences of the Czech Republic, Prag.

Jovanović, M. M., John, M., & Reschke, S. (2010). Effects of civil war: scientific cooperation in the republics of the former Yugoslavia and the province of Kosovo. Scientometrics, 82(3), 627–645. https://doi.org/10.1007/s11192-010-0176-x