Daniel Kwakye

I am

About

I am a PhD researcher working on knowledge graph generation for wind energy within TWEED, a Marie Skłodowska-Curie Doctoral Network on wind energy digitalisation. My work is hosted at OST – Eastern Switzerland University of Applied Sciences (Institute for Energy Technology) and I am matriculated at ETH Zurich in the Chair of Structural Mechanics and Monitoring.

Daniel Kwakye

Researcher in knowledge engineering & geoinformatics

My interests sit at the intersection of computational sustainability, semantic web technologies and the quality of open geospatial data.

  • Website: dkwakye.github.io
  • Based in: Rapperswil / Zurich, Switzerland
  • Role: PhD Researcher · OST / ETH Zurich

Research interests: knowledge graphs, ontology engineering and autonomous ontology evolution, the FAIR data principles, and the analysis of volunteered geographic information such as OpenStreetMap.

Publications

Selected peer-reviewed work. Full list on Google Scholar.

Accelerating the Adoption of Knowledge Graphs for Cross-Disciplinary Collaboration in Wind Energy

2026

Kwakye, D. & Barber, S. — Journal of Physics: Conference Series, 3224(6), 062044

A systematic review of sixteen knowledge-graph implementations in wind energy, comparing data models, construction strategies, technology stacks and semantic formalisms. doi:10.1088/1742-6596/3224/6/062044

The changing surface of the world’s roads

2026

Randhawa, S., Randhawa, G., Langer, C., … Andorful, F., Herfort, B. (incl. Kwakye, D.) — Nature Communications, 17, 9345

A deep-learning framework applied to global satellite imagery produces the first multi-temporal global dataset of road pavedness and width for 9.2 million km of arterial roads. arXiv:2512.04092

Data Quality of OpenStreetMap for Industrial Sites in the Arctic

2024

Kwakye, D., Marx, S., Herfort, B., Langer, M. & Lautenbach, S. — AGILE: GIScience Series, 5, 34

Assesses how completely and accurately OpenStreetMap represents industrial infrastructure across the Arctic, using OSM history and the ohsome API. doi:10.5194/agile-giss-5-34-2024

Doctoral Research

Knowledge graphs for wind energy, within the TWEED doctoral network.

Novel methods for a knowledge graph generation approach within TWEED’s Virtual Wind Farm Hub

knowledge graphs · ontology engineering · semantic web · FAIR data · wind energy digitalisation

TWEED (Training Wind Energy Experts on Digitalisation) is a Marie Skłodowska-Curie Doctoral Network under Horizon Europe (Grant No. 101168673, co-funded by the Swiss State Secretariat for Education, Research and Innovation). Coordinated by the University of Zaragoza, it trains twelve doctoral candidates across Europe towards the “Smart Wind Power Plant of the Future” — abundant wind energy at half of today’s cost by 2050 through digitalisation.

My project investigates, designs and implements a largely automated approach for generating knowledge graphs that integrate heterogeneous wind-energy data — SCADA, maintenance records, technical documents — so it can be discovered, linked and reused across disciplines through TWEED’s Virtual Wind Farm Hub. It is hosted at OST (Institute for Energy Technology and Institute for Software) and matriculated at ETH Zurich.

  • Host: OST – Eastern Switzerland University of Applied Sciences
  • Supervision: Dr. Sarah Barber (OST), Prof. Mitra Purandare (OST), Prof. Eleni Chatzi (ETH Zurich)
  • Network: MSCA Doctoral Network TWEED — Horizon Europe, coordinated by University of Zaragoza

TWEED project CORDIS Related review paper

Contact

Feel free to reach out about research, collaboration or GIS / knowledge-graph work.

Location:

Zurich, Switzerland

Profiles:

ETH Zurich — Structural Mechanics and Monitoring
OST — Wind Energy, Institute for Energy Technology