Knowledge Representation for Multi-Disciplinary Co-Design

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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.

PRINCIPAL INVESTIGATORS

Prof. Dr. Steffen Staab
Department for Analytic Computing (AC), Institute for Parallel and Distributed System, 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

TEAM

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

PEER-REVIEWED PUBLICATIONS

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.
  - BibTeX
  BibTeX
  @inproceedings{elshaniknowledge, author = {Elshani, Diellza and Lombardi, Alessio and Fisher, Al and Staab, Steffen and Hernández, Daniel and Wortmann, Thomas}, booktitle = {In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, eventdate = {May 29, 2022}, eventtitle = {LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, month = {05}, preprinturl = {https://www.researchgate.net/profile/Diellza-Elshani/publication/360962332_Knowledge_Graphs_for_Multidisciplinary_Co-Design_Introducing_RDF_to_BHoM/links/6295a34f1117461e03ace1e3/Knowledge-Graphs-for-Multidisciplinary-Co-Design-Introducing-RDF-to-BHoM.pdf}, title = {Knowledge Graphs for Multidisciplinary Co-Design: Introducing RDF to BHoM}, venue = {Hersonissos, Greece}, year = 2022 }
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.
  - BibTeX
  BibTeX
  @inproceedings{elshani2022inferential, author = {Elshani, Diellza and Lombardi, Alessio and Fisher, Al and Staab, Steffen and Hernández, Daniel and Wortmann, Thomas}, booktitle = { Proceedings of 33. Forum Bauinformatik }, eventdate = {September 7-9, 2022}, month = {09}, preprinturl = {https://www.researchgate.net/publication/363367234_Inferential_Reasoning_in_Co-Design_Using_Semantic_Web_Standards_alongside_BHoM}, title = {Inferential Reasoning in Co-Design Using Semantic Web Standards alongside BHoM}, venue = {Munich, Germany}, year = 2022 }
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.
  - BibTeX
  BibTeX
  @inproceedings{elshani2022towards, author = {Elshani, Diellza and Wortmann, Thomas and Staab, Steffen}, booktitle = {In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, eventdate = {May 29, 2022}, eventtitle = {LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, month = {05}, preprinturl = {https://www.researchgate.net/publication/360962321_Towards_Better_Co-Design_with_Disciplinary_Ontologies_Review_and_Evaluation_of_Data_Interoperability_in_the_AEC_Industry}, title = {Towards Better Co-Design with Disciplinary Ontologies: Review and Evaluation of Data Interoperability in the AEC Industry.}, venue = {Hersonissos, Greece}, year = 2022 }
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
  - BibTeX
  BibTeX
  @article{wortmann2022bauplanung, author = {Wortmann, Thomas and Herschel, Melanie and Staab, Steffen and Tarín, Cristina}, doi = {https://doi.org/10.1002/bate.202200070}, journal = {Bautechnik}, number = 10, title = {AI for AEC: KI für Bauplanung und Bau}, volume = 99, year = 2022 }

OTHER PUBLICATIONS

2023
1. 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
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/www/Galarraga0KH23, abstract = {Knowledge graphs (KGs) are vast collections of machine-readable information, usually modeled in RDF and queried with SPARQL. KGs have opened the door to a plethora of applications such as Web search or smart assistants that query and process the knowledge contained in those KGs. An important, but often disregarded, aspect of querying KGs is query provenance: explanations of the data sources and transformations that made a query result possible. In this article we demonstrate, through a Web application, the capabilities of SPARQLprov, an engine-agnostic method that annotates query results with how-provenance annotations. To this end, SPARQLprov resorts to query rewriting techniques, which make it applicable to already deployed SPARQL endpoints. We describe the principles behind SPARQLprov and discuss perspectives on visualizing how-provenance explanations for SPARQL queries. }, author = {Galárraga, Luis and Hernández, Daniel and Katim, Anas and Hose, Katja}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}, doi = {10.1145/3543873.3587350}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan F. and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023, WWW 2023}, language = {English}, pages = {212-216}, publisher = {ACM}, title = {Visualizing How-Provenance Explanations for SPARQL Queries}, url = {https://doi.org/10.1145/3543873.3587350}, venue = {Austin, Texas, USA}, year = 2023 }
2. Gregucci, C., Nayyeri, M., Hernández, D., & Staab, S. (2023). Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models (Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben, Eds.). ACM. https://doi.org/10.1145/3543507.3583358
  - BibTeX
  BibTeX
  @proceedings{gregucci2023link, abstract = {Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be learned, e.g., some relations might be symmetric and others might be hierarchical. However, the learning capability of different embedding models varies for each pattern and, so far, no single model can learn all patterns equally well. In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. Our combination uses attention to select the most suitable model to answer each query. The models are also mapped onto a non-Euclidean manifold, the Poincaré ball, to capture structural patterns, such as hierarchies, besides relational patterns, such as symmetry. We prove that our combination provides a higher expressiveness and inference power than each model on its own. As a result, the combined model can learn relational and structural patterns. We conduct extensive experimental analysis with various link prediction benchmarks showing that the combined model outperforms individual models, including state-of-the-art approaches.}, archiveprefix = {arXiv}, author = {Gregucci, Cosimo and Nayyeri, Mojtaba and Hernández, Daniel and Staab, Steffen}, doi = {10.1145/3543507.3583358}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan F. and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eprint = {2302.06229}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023}, language = {English}, primaryclass = {cs.AI}, publisher = {ACM}, title = {Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models}, url = {https://doi.org/10.1145/3543507.3583358}, venue = {Austin, Texas, USA}, year = 2023 }

DATA SETS

2023
1. Elshani, D. (2023). BHoM to bhOWL converter. DaRUS. https://doi.org/10.18419/darus-3364
  - BibTeX
  BibTeX
  @dataset{darus-3364_2023, author = {Elshani, Diellza}, doi = {10.18419/darus-3364}, publisher = {DaRUS}, title = {{BHoM to bhOWL converter}}, url = {https://doi.org/10.18419/darus-3364}, version = {V1}, year = 2023 }

Prof. Dr. Steffen Staab

Participating Researcher

Phone: +49 711 685 88100

E-Mail

Tenure-Track Prof. Dr. Thomas Wortmann

Cluster Professor

Phone: +49 711 685 81933

E-Mail

PRINCIPAL INVESTIGATORS

TEAM

PEER-REVIEWED PUBLICATIONS

2022

BibTeX

BibTeX

BibTeX

BibTeX

OTHER PUBLICATIONS

2023

BibTeX

BibTeX

DATA SETS

2023

BibTeX

Prof. Dr. Steffen Staab

Tenure-Track Prof. Dr. Thomas Wortmann

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Organization

Knowledge Representation for Multi-Disciplinary Co-Design

PRINCIPAL INVESTIGATORS

TEAM

PEER-REVIEWED PUBLICATIONS

2022

BibTeX

BibTeX

BibTeX

BibTeX

OTHER PUBLICATIONS

2023

BibTeX

BibTeX

DATA SETS

2023

BibTeX

Prof. Dr. Steffen Staab

Tenure-Track Prof. Dr. Thomas Wortmann

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Formalities

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