Knowledge Representation for Multi-Disciplinary Co-Design

Research Project 20-1 (RP 20-1)

KNOWLEDGE REPRESENTATION FOR MULTI-DISCIPLINARY CO-DESIGN OF BUILDINGS

Architecture, Engineering, and Construction (AEC) projects require multi-disciplinary solutions, making the AEC industry fragmented. In a co-design approach, data from disciplines must be integrated and interoperable. In current practice, interoperability problems between different software often hinder this integration. These interoperability issues may prevent the recognition of violated design constraints until it is too late, i.e., until construction has already started.

This project aims to develop a novel, knowledge-driven framework and design methodology supported by reasoning methods that allow for constraint-checking and other design process support over a closed co-design loop, including architectural design, structural design, building physics-acoustics, and prefabrication of building parts. Within the project frame, we develop disciplinary core ontologies connected by a joint top-level ontology based on background research on existing data models, requirements from AEC, and new possibilities in computer science, specifically, knowledge representation, reasoning, and model-driven software engineering.

The project employs the cluster of excellence as a model for multi-disciplinary co-design processes to rethink data and knowledge representation. It implements example knowledge bases based on the developed ontologies. Additionally, it evaluates the developed ontologies are by applying them to selected building design and/or parts of the demonstrator projects developed in the cluster of excellence.

 

PARTICIPATING RESEARCHERS

Prof. Dr. Steffen Staab
Institute for Artificial Intelligence (KI), University of Stuttgart
Tenure-Track Prof. Dr. Thomas Wortmann
Chair for Computing in Architecture, Institute for Computational Design and Construction (ICD-CA), University of Stuttgart 
Prof. Dr. Mathias Niepert
Institute for Artificial Intelligence (KI), University of Stuttgart

TEAM

Dr. Daniel Hérnandez (KI)
Diellza Elshani (ICD)

 

PEER-REVIEWED PUBLICATIONS

  1. 2024

    1. Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, May). NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645557
    2. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024, May). From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645550
  2. 2023

    1. Elshani, D., Hernandez, D., Lombardi, A., Siriwardena, L., Schwinn, T., Fisher, A., Staab, S., Menges, A., & Wortmann, T. (2023). Building Information Validation and Reasoning Using Semantic Web Technologies. In M. Turrin, C. Andriotis, & A. Rafiee (Eds.), Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries (pp. 470--484). Springer Nature Switzerland.
    2. Galárraga, L., Hernández, D., Katim, A., & Hose, K. (2023). Visualizing How-Provenance Explanations for SPARQL Queries. In Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben (Eds.), Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 (pp. 212–216). ACM. https://doi.org/10.1145/3543873.3587350
    3. Gregucci, C., Nayyeri, M., Hernández, D., & Staab, S. (2023). Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models. In Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben (Eds.), Proceedings of the ACM Web Conference 2023 (pp. 2600–2610). Association for Computing Machinery. https://doi.org/10.1145/3543507.3583358
  3. 2022

    1. Elshani, D., Lombardi, A., Fisher, A., Staab, S., Hernández, D., & Wortmann, T. (2022, May). Knowledge Graphs for Multidisciplinary Co-Design: Introducing RDF to BHoM. In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop. LDAC2022 - 10th Linked Data in Architecture and Construction Workshop, Hersonissos, Greece.
    2. Elshani, D., Lombardi, A., Fisher, A., Staab, S., Hernández, D., & Wortmann, T. (2022, September). Inferential Reasoning in Co-Design Using Semantic Web Standards alongside BHoM. Proceedings of 33. Forum Bauinformatik.
    3. Elshani, D., Wortmann, T., & Staab, S. (2022, May). Towards Better Co-Design with Disciplinary Ontologies: Review and Evaluation of Data Interoperability in the AEC Industry. In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop. LDAC2022 - 10th Linked Data in Architecture and Construction Workshop, Hersonissos, Greece.
    4. Wortmann, T., Herschel, M., Staab, S., & Tarín, C. (2022). AI for AEC: KI für Bauplanung und Bau. Bautechnik, 99(10), Article 10. https://doi.org/10.1002/bate.202200070

OTHER PUBLICATIONS

    DATA SETS

    1. 2024

      1. Asma, Z., Hernandez, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). Code and benchmark for NPCS, a Native Provenance Computation for SPARQL. DaRUS. https://doi.org/10.18419/darus-3973
      2. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Code for From Shapes to Shapes. DaRUS. https://doi.org/10.18419/darus-3977
    2. 2023

      1. Elshani, D., Lombardi, A., Hernández, D., Staab, S., Fisher, A., & Wortmann, T. (2023). BHoM to bhOWL converter. DaRUS. https://doi.org/10.18419/darus-3364

        

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