Knowledge Representation for Multi-Disciplinary Co-Design

IntCDC Research Project 20-1 – Knowledge Representation for Multi-Disciplinary Co-Design

KNOWLEDGE REPRESENTATION FOR MULTI-DISCIPLINARY CO-DESIGN OF BUILDINGS

Architecture, Engineering and Construction (AEC) projects require multi-disciplinary solutions, which has fragmented the AEC industry. In a co-design approach, data from different disciplines need to be integrated and interoperable. In current practice, interoperability issues between different software often hinder this integration. These interoperability issues can prevent the discovery of breached design constraints until it is too late and construction has already begun.

This project aims to develop a novel, knowledge-driven framework and design methodology, supported by reasoning methods, to enable for constraint checking and other design process support in a closed co-design loop, including architectural design, structural design, building physics-acoustics, and prefabrication of building parts. The project will develop core disciplinary ontologies, connected by a common top-level ontology, based on background research on existing data models, requirements from AEC and new opportunities from computer science, in particular knowledge representation, reasoning and model-driven software engineering.

The project uses 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. In addition, it evaluates the developed ontologies by applying them to the 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
Institute for Computational Design and Construction (ICD), 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

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
  - BibTeX
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  @inproceedings{zubaria2024native, abstract = {The popularity of Knowledge Graphs (KGs) both in industry and academia owes credit to their flexible data model, suitable for data integration from multiple sources. Several KG-based applications such as trust assessment or view maintenance on dynamic data rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. By building upon spm-provenance semirings, NPCS supports both monotonic and non-monotonic SPARQL queries. Thanks to its reliance on query rewriting techniques, the approach is directly applicable to already deployed SPARQL engines using different reification schemes -- including RDF-star. Our experimental evaluation on two popular SPARQL engines (GraphDB and Stardog) shows that our novel query rewriting brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.}, author = {Asma, Zubaria and Hernández, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645557}, eventdate = {May 13 -17 2024}, eventtitle = {WWW '24}, isbn = {979-8-4007-0171-9/24/05}, language = {English}, month = {05}, publisher = {ACM}, title = {NPCS: Native Provenance Computation for SPARQL}, url = {https://doi.org/10.1145/3589334.3645557}, venue = {Singapore}, year = 2024 }
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
  - BibTeX
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  @inproceedings{seifer2024shapes, abstract = {SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template. In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is fixed, e.g., being part of a program, whereas the input graphs adhere to input shape constraints but may otherwise vary over time and, thus, are mostly unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment of SHACL (Simple SHACL). We formally define the problem of deriving the most restrictive set of Simple SHACL shapes that constrain the results from evaluating a SCCQ over any input graph restricted by a given set of Simple SHACL shapes. We propose and implement an algorithm that statically analyses input SHACL shapes and CONSTRUCT queries and prove its soundness and complexity.}, author = {Seifer, Philipp and Hernández, Daniel and Lämmel, Ralf and Staab, Steffen}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645550}, eventdate = {May 13–17, 2024}, eventtitle = {WWW '24}, isbn = {979-8-4007-0171-9/24/05}, month = {05}, preprinturl = {https://arxiv.org/abs/2402.08509}, publisher = {ACM}, title = {From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries}, url = {https://doi.org/10.1145/3589334.3645550}, venue = {Singapore}, year = 2024 }
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.
  - BibTeX
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  @inproceedings{10.1007/978-3-031-37189-9_31, abstract = {The integration of data from various disciplines, including requirements and regulations, is essential in the co-design of buildings. Constraints that arise during design, fabrication, or construction are mainly considered in the later stages of the design process. This often leads to costly revisions during fabrication or construction. While there are some works proposing Semantic Web approaches or ad-hoc methods to identify constraint violations in the early stages of design, they mainly rely on data derived from monolithic data schemas. This paper presents a Semantic Web approach that validates and checks building data derived from federated data schemas. The proposed approach is exemplified through the design process of a segmented timber shell, a complex structure requiring negotiations across architectural, engineering, and fabrication parameters. The Building Habitat object Models (BHoM) framework is used to represent federated building data, and the approach enables a seamless movement between the graph environment and object models to support the design process. Additionally, the study discusses various technologies that aid in knowledge inference or data validation. The results of this study suggest that the use of Semantic Web technologies in conjunction with federated data schemas has significant potential to enhance co-design processes in the building industry. While further research is needed to assess its effectiveness in other scenarios, our application of this approach in checking and validating building data for a complex segmented timber shell structure demonstrates its promise as a means to streamline the design process.}, address = {Cham}, author = {Elshani, Diellza and Hernandez, Daniel and Lombardi, Alessio and Siriwardena, Lasath and Schwinn, Tobias and Fisher, Al and Staab, Steffen and Menges, Achim and Wortmann, Thomas}, booktitle = {Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries}, editor = {Turrin, Michela and Andriotis, Charalampos and Rafiee, Azarakhsh}, isbn = {978-3-031-37189-9}, pages = {470--484}, publisher = {Springer Nature Switzerland}, title = {Building Information Validation and Reasoning Using Semantic Web Technologies}, year = 2023 }
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
  - BibTeX
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  @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 }
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
  - BibTeX
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  @inproceedings{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}, booktitle = {Proceedings of the ACM Web Conference 2023}, 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}, pages = {2600-2610}, primaryclass = {cs.AI}, publisher = {Association for Computing Machinery}, series = {WWW '23}, 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 }
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

DATA SETS

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
  - BibTeX
  BibTeX
  @misc{asma2024benchmark, abstract = {Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL.The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenance polynomials (i.e., how-provenance data). The translation rules implemented for the query rewriting can be seen in the paper.Also, the code contains scripts that include scripts and services to automatize the query execution.We use GraphDB (version 10.2.0) and Stardog (version 9.1.0) for the SPARQL endpoints. Because of the license restrictions, these software products cannot be included in this dataset and must be downloaded from the respective vendors. Also, the data must be loaded using the respective bulk loaders of GraphDB and Stardog.The datasets used in the experiments can be generated synthetic dataset generator of the WatDiv benchmark. The Wikidata dataset corresponds to the full RDF dump from May 22, 2023.Do not hesitate to contact the authors for any inquiries. }, affiliation = {Asma, Zubaria/FORT-ICS, Hernández, Daniel/University of Stuttgart, Galárraga, Luis/INRIA, Flouris, Giorgos/FORTH-ICS, Fundulaki, Irini/FORTH-ICS, Hose, Katja/TU Wien}, author = {Asma, Zubaria and Hernandez, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, doi = {10.18419/darus-3973}, howpublished = {Software}, note = {Related to: A. Zubaria, D. Hernández, L. Galárraga, G. Flouris, I. Fundulaki, and K. Hose. NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW '24), May13-17, 2024, Singapore, Singapore, ACM, (May 2024). doi: 10.1145/3589334.3645557}, orcid-numbers = {Asma, Zubaria/0000-0002-9402-7487, Hernández, Daniel/0000-0002-7896-0875, Galárraga, Luis/0000-0002-0241-5379, Flouris, Giorgos/0000-0002-8937-4118, Fundulaki, Irini/0000-0002-4812-9896, Hose, Katja/0000-0001-7025-8099}, publisher = {DaRUS}, title = {Code and benchmark for NPCS, a Native Provenance Computation for SPARQL}, url = {https://doi.org/10.18419/darus-3973}, year = 2024 }
2. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Code for From Shapes to Shapes. DaRUS. https://doi.org/10.18419/darus-3977
  - BibTeX
  BibTeX
  @misc{seifer2024shapes, abstract = {This dataset contains the implementation code for an algorithm to infer SHACL shapes that the graph returned by an SPARQL CONSTRUCT query must satisfy if the input satisfies a given set of SHACL shapes. This dataset also includes an evaluation for the algorithm. The algorithm implemented in this dataset is proposed in the paper From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. To execute the code, follow the instructions in the README.md file. For more info, please check the paper, and please have no hesitation to contact the authors for any inquiries. }, affiliation = {Seifer, Philipp/University of Koblenz, Hernández, Daniel/University of Stuttgart, Lämmel, Ralf/University of Koblenz, Staab, Steffen/University of Stuttgart}, author = {Seifer, Philipp and Hernández, Daniel and Lämmel, Ralf and Staab, Steffen}, doi = {10.18419/darus-3977}, howpublished = {Software}, note = {Related to: Philipp Seifer, Daniel Hernández, Ralf Lämmel, and Steffen Staab. 2024. From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. In Proceedings of the ACM Web Conference 2024 (WWW ’24). ACM. doi: 10.1145/3589334.3645550}, orcid-numbers = {Seifer, Philipp/0000-0002-7421-2060, Hernández, Daniel/0000-0002-7896-0875, Lämmel, Ralf/0000-0001-9946-4363, Staab, Steffen/0000-0002-0780-4154}, publisher = {DaRUS}, title = {Code for From Shapes to Shapes}, year = 2024 }
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
  - BibTeX
  BibTeX
  @dataset{darus-3364_2023, author = {Elshani, Diellza and Lombardi, Alessio and Hernández, Daniel and Staab, Steffen and Fisher, Al and Wortmann, Thomas}, 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. Mathias Niepert

Participating Researcher

Phone: +49 711 685 88122

E-Mail

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

KNOWLEDGE REPRESENTATION FOR MULTI-DISCIPLINARY CO-DESIGN OF BUILDINGS

PARTICIPATING RESEARCHERS

TEAM