COFFEE

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 - 04/2025

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

Partner:

University Erlangen-Nürnberg, Lehrstuhl für Konstruktionstechnik (Prof. Dr. Sandro Wartzack)

Publications

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
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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 }
Link
https://doi.org/10.1145/3589334.3645550
Blomqvist, E., García-Castro, R., Hernández, D., Hitzler, P., Lindecrantz, M., & Poveda-Villalón, M. (Eds.). (2024). Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024) (Vol. 3753). Knowledge Graphs for Sustainability 2024, Hersonissos, Greece, May 27th, 2024. CEUR. https://ceur-ws.org/Vol-3753/
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@proceedings{blomqvist2024proceedings, editor = {Blomqvist, Eva and García-Castro, Raúl and Hernández, Daniel and Hitzler, Pascal and Lindecrantz, Mikael and Poveda-Villalón, María}, eventdate = {2024-05-27}, eventtitle = {Knowledge Graphs for Sustainability 2024}, month = {05}, publisher = {CEUR}, title = {Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024)}, url = {https://ceur-ws.org/Vol-3753/}, venue = {Hersonissos, Greece, May 27th, 2024}, volume = 3753, year = 2024 }
Link
https://ceur-ws.org/Vol-3753/
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
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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 = {Zubaria, Asma 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 }
Link
https://doi.org/10.1145/3589334.3645557
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
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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 }
Link
https://doi.org/10.1145/3589334.3645557
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
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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 }
Link
https://doi.org/10.1145/3543873.3587350
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.
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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 }
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
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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.}, author = {Gregucci, Cosimo and Nayyeri, Mojtaba and Hernández, Daniel and Staab, Steffen}, booktitle = {Proceedings of the {ACM} Web Conference 2023, {WWW} 2023, Austin, TX, USA, 30 April 2023 - 4 May 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}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023}, language = {English}, pages = {2600-2610}, preprinturl = {https://doi.org/10.48550/arXiv.2302.06229}, primaryclass = {cs.AI}, publisher = {ACM}, title = {Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models}, type = {Publication}, url = {https://doi.org/10.1145/3543507.3583358}, venue = {Austin, Texas, USA}, year = 2023 }
Link
https://doi.org/10.1145/3543507.3583358
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
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@inproceedings{DBLP:conf/comma/PotykaBSS22, author = {Potyka, Nico and Bazo, Mohamad and Spieler, Jonathan and Staab, Steffen}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {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}, editor = {Kuhlmann, Isabelle and Mumford, Jack and Sarkadi, Stefan}, pages = {96-108}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Learning Gradual Argumentation Frameworks using Meta-heuristics}, url = {https://ceur-ws.org/Vol-3208/paper7.pdf}, volume = 3208, year = 2022 }
Link
https://ceur-ws.org/Vol-3208/paper7.pdf
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
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@inproceedings{noauthororeditor2022faithful, author = {Xiong, Bo and Potyka, Nico and Tran, T.-K. and Nayyeri, Mojtaba and Staab, Steffen}, booktitle = {Proceedings of the 21st International Semantic Web Conference (ISWC 2022)}, pages = {1-16}, preprinturl = {https://arxiv.org/abs/2201.09919}, title = {Faithful Embedding for EL++ Knowledge Bases}, type = {Publication}, url = {https://arxiv.org/abs/2201.09919}, year = 2022 }
Link
https://arxiv.org/abs/2201.09919
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.
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@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 }
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.
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@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 }, 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}, year = 2022 }
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
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@conference{xiong2022pseudoriemannian, author = {Xiong, Bo and Zhu, Shichao and Potyka, Nico and Pan, Shirui and Zhou, Chuan and Staab, Steffen}, booktitle = {Advances in Neural Information Processing Systems}, title = {Pseudo-Riemannian Graph Convolutional Networks}, url = {https://arxiv.org/abs/2106.03134}, year = 2022 }
Link
https://arxiv.org/abs/2106.03134
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
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@inproceedings{potyka2021generalizing, address = {Cham}, affiliation = {Potyka, N (Corresponding Author), Univ Stuttgart, Stuttgart, Germany. Potyka, Nico, Univ Stuttgart, Stuttgart, Germany.}, author = {Potyka, Nico}, booktitle = {Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 16th European Conference, ECSQARU 2021 Prague, Czech Republic, September 21-24, 2021, Proceedings}, doi = {10.1007/978-3-030-86772-0_10}, editor = {Vejnarová, Jiřina and Wilson, Nic}, eventdate = {2021-09-21/2021-09-24}, eventtitle = {16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2021)}, isbn = {{978-3-030-86771-3} and {978-3-030-86772-0}}, language = {eng}, number = 12897, pages = {130-143}, publisher = {Springer}, research-areas = {Computer Science; Mathematics; Science & Technology - Other Topics}, series = {Lecture Notes in Computer Science}, title = {Generalizing Complete Semantics to Bipolar Argumentation Frameworks}, unique-id = {WOS:000711926000010}, venue = {Prague, Czech Republic}, year = 2021 }
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
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@inproceedings{potyka2021interpreting, address = {Palo Alto}, affiliation = {Potyka, N (Corresponding Author), Univ Stuttgart, Univ Str 32, D-70569 Stuttgart, Germany. Potyka, Nico, Univ Stuttgart, Univ Str 32, D-70569 Stuttgart, Germany.}, author = {Potyka, Nico}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, eventdate = {2021-02-02/2021-02-09}, eventtitle = {35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence}, isbn = {978-1-57735-866-4}, language = {eng}, number = {35, 7}, pages = {6463-6470}, publisher = {Association for the Advancement of Artificial Intelligence}, research-areas = {Computer Science; Education & Educational Research}, title = {Interpreting Neural Networks as Quantitative Argumentation Frameworks}, unique-id = {WOS:000680423506065}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/16801}, venue = {Online}, year = 2021 }
Link
https://ojs.aaai.org/index.php/AAAI/article/view/16801
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
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BibTeX
@inproceedings{potyka2020bipolar, affiliation = {Potyka, N (Corresponding Author), Univ Stuttgart, Stuttgart, Germany. Potyka, Nico, Univ Stuttgart, Stuttgart, Germany.}, author = {Potyka, Nico}, booktitle = {Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning}, doi = {10.24963/kr.2020/69}, editor = {Calvanese, Diego and Erdem, Esra and Thielscher, Michael}, eventdate = {2020-09-12/2020-09-18}, eventtitle = {17th International Conference on Principles of Knowledge Representation and Reasoning (KR and R)}, isbn = {978-0-9992411-7-2}, language = {eng}, pages = {677-686}, publisher = {IJCAI Organization}, research-areas = {Computer Science; Science & Technology - Other Topics}, title = {Bipolar Abstract Argumentation with Dual Attacks and Supports}, unique-id = {WOS:000720083100068}, venue = {Online}, year = 2020 }
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
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BibTeX
@inproceedings{potyka20ecai, author = {Potyka, Nico}, booktitle = {European Conference on Artificial Intelligence (ECAI)}, pages = {865-872}, title = {Abstract Argumentation with Markov Networks}, url = {https://www.researchgate.net/publication/338886145_Abstract_Argumentation_with_Markov_Networks}, year = 2020 }
Link
https://www.researchgate.net/publication/338886145_Abstract_Argumentation_with_Markov_Networks

Data and Software

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
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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}, title = {Code and benchmark for NPCS, a Native Provenance Computation for SPARQL}, url = {https://doi.org/10.18419/darus-3973}, year = 2024 }
Link
https://doi.org/10.18419/darus-3973
Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Code for From Shapes to Shapes. 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}, title = {Code for From Shapes to Shapes}, year = 2024 }

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