COFFEE

Collaborative Forgetting for Engineering Design

Collaborative Forgetting for Engineering Design

In engineering, product development is a collaborative activity reusing and adapting existing product models. While capturing design rationale is effective to deal with adaptations by teams, adaptation not only requires joint extension of models, as supported by existing methods for design rationale, but also removing what is no longer relevant or in conflict. Such Intentional Forgetting is difficult, as may affect assemblages of elements, should not over- or underdelete, and may require undoing. While EVOWIPE (our phase 1 project) has successfully tackled IF, project teams require methods for Collaborative Forgetting that is addressed in this project.

Operating Time: 01/2020 - 12/2022

Source of Funding: DFG - Deutsche Forschungsgemeinschaft, Priority Research Programme "Intentional Forgetting in Organisations"

Partner:

Publications

  1. 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. Zubaria, A., 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
  3. 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
  4. 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
  5. 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.
  6. 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, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 (pp. 2600–2610). ACM. https://doi.org/10.1145/3543507.3583358
  7. Potyka, N., Bazo, M., Spieler, J., & Staab, S. (2022). Learning Gradual Argumentation Frameworks using Meta-heuristics. In I. Kuhlmann, J. Mumford, & S. Sarkadi (Eds.), Proceedings of the 1st Workshop on Argumentation & Machine Learning co-located with 9th International Conference on Computational Models of Argument (COMMA 2022), Cardiff, Wales, September 13th, 2022 (Vol. 3208, pp. 96–108). CEUR-WS.org. https://ceur-ws.org/Vol-3208/paper7.pdf
  8. Xiong, B., Potyka, N., Tran, T.-K., Nayyeri, M., & Staab, S. (2022). Faithful Embedding for EL++ Knowledge Bases. Proceedings of the 21st International Semantic Web Conference (ISWC 2022), 1–16. https://arxiv.org/abs/2201.09919
  9. 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.
  10. 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.
  11. Xiong, B., Zhu, S., Potyka, N., Pan, S., Zhou, C., & Staab, S. (2022). Pseudo-Riemannian Graph Convolutional Networks. Advances in Neural Information Processing Systems. https://arxiv.org/abs/2106.03134
  12. Potyka, N. (2021). Generalizing Complete Semantics to Bipolar Argumentation Frameworks. In J. Vejnarová & N. Wilson (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 16th European Conference, ECSQARU 2021 Prague, Czech Republic, September 21-24, 2021, Proceedings (No. 12897; Issue 12897, pp. 130–143). Springer. https://doi.org/10.1007/978-3-030-86772-0_10
  13. Potyka, N. (2021). Interpreting Neural Networks as Quantitative Argumentation Frameworks. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 7, Article 35, 7. https://ojs.aaai.org/index.php/AAAI/article/view/16801
  14. Potyka, N. (2020). Bipolar Abstract Argumentation with Dual Attacks and Supports. In D. Calvanese, E. Erdem, & M. Thielscher (Eds.), Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (pp. 677–686). IJCAI Organization. https://doi.org/10.24963/kr.2020/69
  15. Potyka, N. (2020). Abstract Argumentation with Markov Networks. European Conference on Artificial Intelligence (ECAI), 865–872. https://www.researchgate.net/publication/338886145_Abstract_Argumentation_with_Markov_Networks

Data and Software

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

Team

Former researchers

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