Driving Ecological Building Innovation Through Multi-Disciplinary Data Integration
A research team from Vienna University of Technology used Graphwiset's technology to create an ontology-driven design framework for next-generation multi-species buildings, enabling seamless multi-disciplinary integration and faster, smarter design iteration and innovation.
The Client
Research team from the Department of Digital Architecture and Planning at Vienna University of Technology, part of the European Horizon 2020 ECOLOPES Project Consortium
The Challenge
The team needed to integrate fragmented, multi-disciplinary data from architecture and ecology domains to support early-stage design of ecological buildings
The Solution
Using GraphDB and Ontology-based Data Access (OBDA), the team built the ECOLOPES Knowledge Graph that integrates data silos through both materialization and virtualization approaches
Technical capabilities
- Enabled real-time design feedback in Grasshopper and CAD environments through dynamic graph queries
- Powered advanced reasoning via SPARQL-based rule execution, virtualized spatial models, and live access to relational data using OBDA
Business outcomes
- Faster design iteration and innovation through automated semantic feedback, agile experimentation, and faster data integration
- Improved cross-disciplinary collaboration with shared access to normalized, queryable data
The Challenge
The ECOLOPES European Horizon 2020 project moves beyond anthropocentric design to enable nature’s co-evolution within cities. The consortium combines architects, ecologists and computer scientists to develop an integrative computational framework and tools.
As part of this project, a team of researchers from Vienna University of Technology sought to integrate existing multi-disciplinary data holistically, supporting early-stage design of multi-species buildings and urban sites. Their goal was providing complete views of relevant multi-disciplinary data for effective architect-ecologist collaboration.
Existing data presented obstacles including fragmented datasets across architecture and ecology domains, diverse formats covering buildings to ecological networks, and varying completeness levels requiring normalization.
The Solution
The research team implemented an Ontology-based Data Access (OBDA) framework using GraphDB to integrate data silos within the ECOLOPES Knowledge Graph.
The solution combined two approaches: data materialization and data virtualization. For materialization, plant functional groups, Vienna flora/fauna, and biotic interactions were mapped using Refine and stored in GraphDB. Also, CAD and Grasshopper workflow data were transformed via JSON-LD and integrated through GraphDB’s API. For virtualization, Voxel models storing solar radiation data remained in PostgreSQL but were accessed through OBDA mappings, enabling semantic access without data movement.
This knowledge graph enabled architects to ask complex competency questions (such as “Give me all tree species from genus Abies that interact with a cream wave?” ) via SPARQL queries and receive dynamic answers within Grasshopper workflows. Designers got real-time visual feedback when moving CAD nodes: green circles indicated constraint satisfaction, red crosses showed unmet requirements.re enabled Parliament to remodel how they manage data, making information easily and quickly available for public engagement, analysis, and exploration.
The Impact
The ECOLOPES Knowledge Graph delivered significant advances in ecological design:
- Enhanced decision support enabling architects and planners to work with multi-disciplinary, multi-scalar data seamlessly
- Accelerated innovation through faster access to disparate data sources and quicker design insights
- Streamlined ecological design making it easier to create solutions that transform existing sites into ecological equivalents
The system enables designers to ask complex questions and receive immediate visual feedback, with green circles indicating nodes that satisfy requirements and red crosses showing violations.
“Graphwise and their products GraphDB and Refine have helped us create a knowledge graph in an agile-based manner. Thanks to the OBDA approach, not all data needs to be moved so it can become knowledge. The intuitive, true and tested, and robust interface of GraphDB allows us to get very quick answers to our competency questions before implementing them in our Rhino/Grasshopper workflows.”
Albin Ahmeti, PhD, Department of Digital Architecture and Planning, TU Vienna
Facing Similar Challenges?
Struggling with fragmented datasets slowing innovation, time-consuming manual processes, or difficulty integrating workflows with complex domain knowledge? Whether you're a research institution, architecture firm, urban planning agency, or multi-disciplinary design team, Graphwise can help you: