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Hernández, Daniel

Daniel Hernández

Dr.rer.nat.

Researcher
Institute of Artificial Intelligence
Analytic Computing

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+49 711 685 88123
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Universitätsstraße 32
70569 Stuttgart
Germany
Room: 2.303b

Subject

Semantic Web, SPARQL, RDF, and Databases.

Publications

Publications:

1. G. Demartini et al., Eds., The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part I, vol. 15231. in Lecture Notes in Computer Science, vol. 15231. Springer, 2025. doi: 10.1007/978-3-031-77844-5.
- BibTeX
- Link
BibTeX
@proceedings{DBLP:conf/semweb/2024-1, bibsource = {dblp computer science bibliography, https://dblp.org}, doi = {10.1007/978-3-031-77844-5}, editor = {Demartini, Gianluca and Hose, Katja and Acosta, Maribel and Palmonari, Matteo and Cheng, Gong and Skaf{-}Molli, Hala and Ferranti, Nicolas and Hern{\'{a}}ndez, Daniel and Hogan, Aidan}, isbn = {978-3-031-77843-8}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part I}, url = {https://doi.org/10.1007/978-3-031-77844-5}, volume = 15231, year = 2025 }
Link
https://doi.org/10.1007/978-3-031-77844-5
2. G. Demartini et al., Eds., The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part III, vol. 15233. in Lecture Notes in Computer Science, vol. 15233. Springer, 2025. doi: 10.1007/978-3-031-77847-6.
- BibTeX
- Link
BibTeX
@proceedings{DBLP:conf/semweb/2024-3, bibsource = {dblp computer science bibliography, https://dblp.org}, doi = {10.1007/978-3-031-77847-6}, editor = {Demartini, Gianluca and Hose, Katja and Acosta, Maribel and Palmonari, Matteo and Cheng, Gong and Skaf{-}Molli, Hala and Ferranti, Nicolas and Hern{\'{a}}ndez, Daniel and Hogan, Aidan}, isbn = {978-3-031-77846-9}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part III}, url = {https://doi.org/10.1007/978-3-031-77847-6}, volume = 15233, year = 2025 }
Link
https://doi.org/10.1007/978-3-031-77847-6
3. G. Demartini et al., Eds., The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part II, vol. 15232. in Lecture Notes in Computer Science, vol. 15232. Springer, 2025. doi: 10.1007/978-3-031-77850-6.
- BibTeX
- Link
BibTeX
@proceedings{DBLP:conf/semweb/2024-2, bibsource = {dblp computer science bibliography, https://dblp.org}, doi = {10.1007/978-3-031-77850-6}, editor = {Demartini, Gianluca and Hose, Katja and Acosta, Maribel and Palmonari, Matteo and Cheng, Gong and Skaf{-}Molli, Hala and Ferranti, Nicolas and Hern{\'{a}}ndez, Daniel and Hogan, Aidan}, isbn = {978-3-031-77849-0}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Semantic Web - ISWC 2024 - 23rd International Semantic Web Conference, Baltimore, MD, USA, November 11-15, 2024, Proceedings, Part II}, url = {https://doi.org/10.1007/978-3-031-77850-6}, volume = 15232, year = 2025 }
Link
https://doi.org/10.1007/978-3-031-77850-6
4. Y. He, B. Xiong, D. Hernández, Y. Zhu, E. Kharlamov, and S. Staab, “DAGE: DAG Query Answering via Relational Combinator with Logical Constraints,” in THE WEB CONFERENCE 2025, ACM, 2025. [Online]. Available: https://openreview.net/forum?id=x1nXBzUknn
Abstract
Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the SROI^- description logic. In this paper, we define a more general set of queries, called DAG queries and formulated in the ALCOIR description logic, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. Given the computational graph of a DAG query, DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator that represents the intersection of relations and learns DAG-DL from tautologies. We show that it is possible to implement DAGE on top of existing query embedding methods, and we empirically measure the improvement of our method over the results of vanilla methods evaluated in tree-form queries that approximate the DAG queries of our proposed benchmark.
BibTeX
@inproceedings{he2025dage, abstract = {Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the SROI^- description logic. In this paper, we define a more general set of queries, called DAG queries and formulated in the ALCOIR description logic, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. Given the computational graph of a DAG query, DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator that represents the intersection of relations and learns DAG-DL from tautologies. We show that it is possible to implement DAGE on top of existing query embedding methods, and we empirically measure the improvement of our method over the results of vanilla methods evaluated in tree-form queries that approximate the DAG queries of our proposed benchmark.}, author = {He, Yunjie and Xiong, Bo and Hern{\'a}ndez, Daniel and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {THE WEB CONFERENCE 2025}, language = {en}, preprinturl = {https://doi.org/10.48550/arXiv.2410.22105}, publisher = {ACM}, title = {DAGE: DAG Query Answering via Relational Combinator with Logical Constraints}, url = {https://openreview.net/forum?id=x1nXBzUknn}, year = 2025 }
Link
https://openreview.net/forum?id=x1nXBzUknn
5. A. Elhalawati, A. Dimou, O. Hartig, and D. Hernández, “Flexible RML-Based Mapping of Property Graphs to RDF,” in Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference (March 25-28, 2025), Barcelona, Spain, S. Dumbrava and R. Tommasini, Eds., CEUR, 2025.
- Abstract
- BibTeX
Abstract
RDF graphs and (Labeled) Property Graphs (PGs) have emerged as data models for representing graph databases. Given the differences between the two models, ensuring interoperability between them has become essential, to leverage the strengths of both models. Various approaches have been proposed to map PGs to RDF graphs. However, these approaches differ in terms of structure, representation, size of the generated RDF graph, and degree of configuration provided to the user, making direct comparisons challenging. While declarative methods prevailed to construct RDF graphs from other data formats, the mapping languages proposed for such transformations have not been considered so far for mapping PGs to RDF graphs. In this work, we provide a representation of PG-to-RDF approaches through templates described using RML, a mapping language to construct RDF graphs from heterogeneous data. We show that all considered PG-to-RDF approaches can be represented in RML and, by having a uniform representation of them, we can compare them showcasing their differences. Finally, we show that not only can RML be used to capture PG-to-RDF mappings, but it actually offers more expressive power than the considered PG-to-RDF approaches.
BibTeX
@inproceedings{elhalawati2025flexible, abstract = {RDF graphs and (Labeled) Property Graphs (PGs) have emerged as data models for representing graph databases. Given the differences between the two models, ensuring interoperability between them has become essential, to leverage the strengths of both models. Various approaches have been proposed to map PGs to RDF graphs. However, these approaches differ in terms of structure, representation, size of the generated RDF graph, and degree of configuration provided to the user, making direct comparisons challenging. While declarative methods prevailed to construct RDF graphs from other data formats, the mapping languages proposed for such transformations have not been considered so far for mapping PGs to RDF graphs. In this work, we provide a representation of PG-to-RDF approaches through templates described using RML, a mapping language to construct RDF graphs from heterogeneous data. We show that all considered PG-to-RDF approaches can be represented in RML and, by having a uniform representation of them, we can compare them showcasing their differences. Finally, we show that not only can RML be used to capture PG-to-RDF mappings, but it actually offers more expressive power than the considered PG-to-RDF approaches.}, author = {Elhalawati, Ali and Dimou, Anastasia and Hartig, Olaf and Hernández, Daniel}, booktitle = {Proceedings of the Workshops of the EDBT/ICDT 2025 Joint Conference (March 25-28, 2025), Barcelona, Spain}, editor = {Dumbrava, Stefania and Tommasini, Riccardo}, eventdate = {March 25, 2025}, eventtitle = {Transforming Graph Data Workshop}, language = {English}, publisher = {CEUR}, title = {Flexible RML-Based Mapping of Property Graphs to RDF}, venue = {Barcelona, Spain}, year = 2025 }
6. Y. He et al., “Generating SROI^- Ontologies via Knowledge Graph Query Embedding Learning,” in ECAI 2024, IOS Press, Oct. 2024, pp. 4279–4286. doi: 10.3233/FAIA241002.
Abstract
Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily rely on excessively parameterized neural networks and cannot explain the knowledge learned from the graph. We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of SROI− description logic axioms while being more parameter-efficient than most existing approaches. AConE associates queries to SROI− description logic concepts. Every SROI− concept is embedded as a cone in complex vector space, and each SROI− relation is embedded as a transformation that rotates and scales cones. We show theoretically that AConE can learn SROI− axioms, and defines an algebra whose operations correspond one-to-one to SROI− description logic concept constructs. Our empirical study on multiple query datasets shows that AConE achieves superior results over previous baselines with fewer parameters. Notably on the WN18RR dataset, AConE achieves significant improvement over baseline models. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering.
BibTeX
@inproceedings{he2024generating, abstract = {Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily rely on excessively parameterized neural networks and cannot explain the knowledge learned from the graph. We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of SROI− description logic axioms while being more parameter-efficient than most existing approaches. AConE associates queries to SROI− description logic concepts. Every SROI− concept is embedded as a cone in complex vector space, and each SROI− relation is embedded as a transformation that rotates and scales cones. We show theoretically that AConE can learn SROI− axioms, and defines an algebra whose operations correspond one-to-one to SROI− description logic concept constructs. Our empirical study on multiple query datasets shows that AConE achieves superior results over previous baselines with fewer parameters. Notably on the WN18RR dataset, AConE achieves significant improvement over baseline models. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering.}, author = {He, Yunjie and Hernandez, Daniel and Nayyeri, Mojtaba and Xiong, Bo and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {ECAI 2024}, doi = {10.3233/FAIA241002}, isbn = {9781643685489}, issn = {1879-8314}, language = {en}, month = {10}, pages = {4279 - 4286}, publisher = {IOS Press}, title = {Generating SROI^{-} Ontologies via Knowledge Graph Query Embedding Learning}, type = {Publication}, url = {https://arxiv.org/abs/2407.09212}, volume = 392, year = 2024 }
Link
https://arxiv.org/abs/2407.09212
7. E. Blomqvist, R. García-Castro, D. Hernández, P. Hitzler, M. Lindecrantz, and M. Poveda-Villalón, Eds., 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. CEUR, 2024. [Online]. Available: https://ceur-ws.org/Vol-3753/
- BibTeX
- Link
BibTeX
@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/
8. Z. Asma, D. Hernández, L. Galárraga, G. Flouris, I. Fundulaki, and K. Hose, “NPCS: Native Provenance Computation for SPARQL,” in Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore, ACM, May 2024. doi: 10.1145/3589334.3645557.
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.
BibTeX
@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
9. P. Seifer, D. Hernández, R. Lämmel, and S. Staab, “From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries,” in Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore, ACM, May 2024. doi: 10.1145/3589334.3645550.
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.
BibTeX
@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 = {13 May 2024 - 17 May 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
10. D. Elshani, A. Dervishaj, D. Hernández, K. Gudmundsson, S. Staab, and T. Wortmann, “An Ontology for the Reuse and Tracking of Prefabricated Building Components,” in Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024), CEUR, May 2024, pp. 53–64. [Online]. Available: https://ceur-ws.org/Vol-3753/paper5.pdf
- BibTeX
- Link
BibTeX
@inproceedings{elshani2024ontology, author = {Elshani, Diellza and Dervishaj, Arlind and Hernández, Daniel and Gudmundsson, Kjartan and Staab, Steffen and Wortmann, Thomas}, booktitle = {Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024)}, eventdate = {2024-05-27}, eventtitle = {Knowledge Graphs for Sustainability 2024}, month = {05}, pages = {53-64}, publisher = {CEUR}, title = {An Ontology for the Reuse and Tracking of Prefabricated Building Components}, url = {https://ceur-ws.org/Vol-3753/paper5.pdf}, venue = {Hersonissos, Greece, May 27th, 2024}, volume = 3753, year = 2024 }
Link
https://ceur-ws.org/Vol-3753/paper5.pdf
11. A. Zubaria, D. Hernández, L. Galárraga, G. Flouris, I. Fundulaki, and K. Hose, “NPCS: Native Provenance Computation for SPARQL,” in Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore, ACM, May 2024. doi: 10.1145/3589334.3645557.
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*. 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.
BibTeX
@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*. 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
12. X. Pan, D. Hernández, P. Seifer, R. Lämmel, and S. Staab, “eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs,” 2024. [Online]. Available: https://arxiv.org/abs/2407.21483
- BibTeX
- Link
BibTeX
@preprint{pan2024esparqlrepresentingreconcilingagnostic, archiveprefix = {arXiv}, author = {Pan, Xiny and Hernández, Daniel and Seifer, Philipp and Lämmel, Ralf and Staab, Steffen}, eprint = {2407.21483}, primaryclass = {cs.AI}, title = {eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs}, url = {https://arxiv.org/abs/2407.21483}, year = 2024 }
Link
https://arxiv.org/abs/2407.21483
13. P. Seifer, D. Hernández, R. Lämmel, and S. Staab, “Code for From Shapes to Shapes.” 2024. doi: 10.18419/darus-3977.
- Abstract
- BibTeX
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.
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 }
14. Z. Asma, D. Hernandez, L. Galárraga, G. Flouris, I. Fundulaki, and K. Hose, “Code and benchmark for NPCS, a Native Provenance Computation for SPARQL.” 2024. doi: 10.18419/darus-3973.
- Abstract
- BibTeX
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.
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}, year = 2024 }
15. P. Seifer, D. Hernández, R. Lämmel, and S. Staab, “Inferring SHACL Constraints for Results of Composable Graph Queries (Extended Abstract),” in Proceedings of the 37th International Workshop on Description Logics (DL 2024), Bergen, Norway, June 18-21, 2024, L. Giordano, J. C. Jung, and A. Ozaki, Eds., in CEUR Workshop Proceedings, vol. 3739. CEUR-WS.org, 2024. [Online]. Available: https://ceur-ws.org/Vol-3739/abstract-23.pdf
Abstract
SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. Input graphs are now commonly constrained through SHACL shapes allowing for both their validation and aiding users (as well as tools) in understanding their structure. 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 itself. In our recent work, From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries, we studied the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query by axiomatizing the query and the shapes with the ALCHOI description logic. This extended abstract summarizes our previous work.
BibTeX
@inproceedings{DBLP:conf/dlog/Seifer0LS24, abstract = {SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. Input graphs are now commonly constrained through SHACL shapes allowing for both their validation and aiding users (as well as tools) in understanding their structure. 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 itself. In our recent work, From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries, we studied the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query by axiomatizing the query and the shapes with the ALCHOI description logic. This extended abstract summarizes our previous work.}, author = {Seifer, Philipp and Hern{\'{a}}ndez, Daniel and L{\"{a}}mmel, Ralf and Staab, Steffen}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 37th International Workshop on Description Logics {(DL} 2024), Bergen, Norway, June 18-21, 2024}, editor = {Giordano, Laura and Jung, Jean Christoph and Ozaki, Ana}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Inferring {SHACL} Constraints for Results of Composable Graph Queries (Extended Abstract)}, url = {https://ceur-ws.org/Vol-3739/abstract-23.pdf}, volume = 3739, year = 2024 }
Link
https://ceur-ws.org/Vol-3739/abstract-23.pdf
16. D. Elshani, A. Lombardi, D. Hernández, S. Staab, A. Fisher, and T. Wortmann, “BHoM to bhOWL converter.” 2023. doi: 10.18419/darus-3364.
- Abstract
- BibTeX
Abstract
The dataset is the release version v2.0.0 of the BHoM to bhOWL converter, which helps convert BHoM data to a knowledge graph in any software BHoM supports. BHoM (The Buildings and Habitats object Model) is collaborative framework that runs within several AEC design software, which helps to represent data in a object oriented database model. OWL (Web Ontology Language) provides a standardized and expressive language for representing knowledge and relationships within a domain. It allows for the creation of ontologies, which are formal descriptions of the concepts and relationships within a domain. Additionally, OWL can support reasoning and inference over ontologies, allowing for automated reasoning about the relationships between different concepts and data elements. This can be particularly useful in identifying inconsistencies or gaps in data, or in suggesting additional data sources or mappings that may be needed to support integration efforts.Our BHoM to bhOWL converter is developed as an extension to BHoM framework, and runs within any software that BHoM supports (eg. Grasshopper 3D, Excel, etc.) The dataset contains also example Grasshopper 3D files that exemplify the process of converting BHoM data to bhOWL.
BibTeX
@misc{elshani2023bhowl, abstract = {The dataset is the release version v2.0.0 of the BHoM to bhOWL converter, which helps convert BHoM data to a knowledge graph in any software BHoM supports. BHoM (The Buildings and Habitats object Model) is collaborative framework that runs within several AEC design software, which helps to represent data in a object oriented database model. OWL (Web Ontology Language) provides a standardized and expressive language for representing knowledge and relationships within a domain. It allows for the creation of ontologies, which are formal descriptions of the concepts and relationships within a domain. Additionally, OWL can support reasoning and inference over ontologies, allowing for automated reasoning about the relationships between different concepts and data elements. This can be particularly useful in identifying inconsistencies or gaps in data, or in suggesting additional data sources or mappings that may be needed to support integration efforts.Our BHoM to bhOWL converter is developed as an extension to BHoM framework, and runs within any software that BHoM supports (eg. Grasshopper 3D, Excel, etc.) The dataset contains also example Grasshopper 3D files that exemplify the process of converting BHoM data to bhOWL. }, affiliation = {Elshani, Diellza/University of Stuttgart, Lombardi, Alessio/Buro Happold, Hernández, Daniel/Department for Analytic Computing (AC), Institute for Parallel and Distributed Systems (IPVS), University of Stuttgart, Staab, Steffen/Department for Analytic Computing (AC), Institute for Parallel and Distributed Systems (IPVS), University of Stuttgart, Fisher, Al/Buro Happold, Wortmann, Thomas/University of Stuttgart}, author = {Elshani, Diellza and Lombardi, Alessio and Hernández, Daniel and Staab, Steffen and Fisher, Al and Wortmann, Thomas}, doi = {10.18419/darus-3364}, howpublished = {Software}, note = {Related to: Elshani, D., Lombardi, A., Fisher, A., Staab, S., Hernández, D., & Wortmann, T. (2022). Knowledge Graphs for Multidisciplinary Co-Design: Introducing RDF to BHoM. In: Proceedings of the 10th Linked Data in Architecture and Construction Workshop (LDAC 2022). CEUR Workshop Proceedings 3213, pp. 32-42. url: https://ceur-ws.org/Vol-3213/paper03.pdf}, orcid-numbers = {Elshani, Diellza/0000-0003-2902-341X, Hernández, Daniel/0000-0002-7896-0875, Staab, Steffen/0000-0002-0780-4154, Wortmann, Thomas/0000-0002-5604-1624}, title = {BHoM to bhOWL converter}, year = 2023 }
17. C. Gregucci, M. Nayyeri, D. Hernández, and S. Staab, “Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models,” in WWW ’23 : Proceedings of the ACM Web Conference 2023, Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, and G.-J. Houben, Eds., Association for Computing Machinery, 2023, pp. 2600–2610. doi: 10.1145/3543507.3583358.
- BibTeX
BibTeX
@inproceedings{gregucci2023prediction, address = {New York}, author = {Gregucci, Cosimo and Nayyeri, Mojtaba and Hernández, Daniel and Staab, Steffen}, booktitle = {WWW '23 : 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}, eventdate = {2023-04-30/2023-05-04}, eventtitle = {WWW '23 : The ACM Web Conference 2023}, isbn = {978-1-4503-9416-1}, language = {eng}, pages = {2600-2610}, publisher = {Association for Computing Machinery}, title = {Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models}, venue = {Austin, USA}, year = 2023 }
18. L. Galárraga, D. Hernández, A. Katim, and K. Hose, “Visualizing How-Provenance Explanations for SPARQL Queries,” in Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023, Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, and G.-J. Houben, Eds., ACM, 2023, pp. 212–216. doi: 10.1145/3543873.3587350.
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.
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 }
Link
https://doi.org/10.1145/3543873.3587350
19. D. Elshani et al., “Building Information Validation and Reasoning Using Semantic Web Technologies,” in Computer-Aided Architectural Design. INTERCONNECTIONS : Co-computing Beyond Boundaries, M. Turrin, C. Andriotis, and A. Rafiee, Eds., in Communications in Computer and Information Science. Springer, 2023, pp. 470–484. doi: 10.1007/978-3-031-37189-9_31.
- BibTeX
BibTeX
@inproceedings{elshani2023building, 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}, doi = {10.1007/978-3-031-37189-9_31}, editor = {Turrin, Michela and Andriotis, Charalampos and Rafiee, Azarakhsh}, eventdate = {2023-07-05/2023-07-07}, eventtitle = {CAAD Futures 2023}, isbn = {{978-3-031-37188-2} and {978-3-031-37189-9}}, language = {eng}, number = 1819, pages = {470-484}, publisher = {Springer}, series = {Communications in Computer and Information Science}, title = {Building Information Validation and Reasoning Using Semantic Web Technologies}, venue = {Delft, The Netherlands}, year = 2023 }
20. L. Galárraga, A. Katim, D. Hernandez, and K. Hose, “Visualizing How-Provenance Explanations for SPARQL Queries,” in Proceedings of the ACM Web Conference 2023, Y. Ding, J. Tang, J. Sequeda, L. Aroyo, C. Castillo, and G.-J. Houben, Eds., Association for Computing Machinery, 2023, pp. 212–216. doi: 10.1145/3543873.3587350.
- BibTeX
BibTeX
@inproceedings{galarraga2023visualizing, address = {New York, NY, United States}, affiliation = {Galárraga, L (Corresponding Author), INRIA, Paris, France. Galarraga, Luis, INRIA, Paris, France. Katim, Anas, INSA Rouen, Rouen, France. Hernandez, Daniel, Univ Stuttgart, Stuttgart, Germany. Hose, Katja, Aalborg Univ, Aalborg, Denmark. Hose, Katja, TU Wien, Vienna, Austria.}, author = {Galárraga, Luis and Katim, Anas and Hernandez, Daniel and Hose, Katja}, booktitle = {Proceedings of the ACM Web Conference 2023}, doi = {10.1145/3543873.3587350}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eventdate = {2023-04-30/2023-05-04}, eventtitle = {32nd World Wide Web Conference (WWW)}, isbn = {978-1-4503-9416-1}, language = {eng}, orcid-numbers = {Hernández, Daniel/0000-0002-7896-0875}, pages = {212-216}, publisher = {Association for Computing Machinery}, researcherid-numbers = {Hernández, Daniel/ABC-4444-2021}, title = {Visualizing How-Provenance Explanations for SPARQL Queries}, unique-id = {WOS:001124276300052}, venue = {Austin, TX}, year = 2023 }
21. D. Elshani, A. Lombardi, A. Fisher, S. Staab, D. Hernández, and T. Wortmann, “Knowledge Graphs for Multidisciplinary Co-Design : Introducing RDF to BHoM,” in Proceedings of the 10th Linked Data in Architecture and Construction Workshop, P. Pauwels, M. Poveda-Villalon, and W. Terkaj, Eds., in CEUR workshop proceedings. RWTH Aachen, 2022, pp. 43–52. [Online]. Available: https://ceur-ws.org/Vol-3213/
- BibTeX
- Link
BibTeX
@inproceedings{elshani2022knowledge, address = {Aachen}, author = {Elshani, Diellza and Lombardi, Alessio and Fisher, Al and Staab, Steffen and Hernández, Daniel and Wortmann, Thomas}, booktitle = {Proceedings of the 10th Linked Data in Architecture and Construction Workshop}, editor = {Pauwels, Pieter and Poveda-Villalon, Maria and Terkaj, Walter}, eventdate = {2022-05-29}, eventtitle = {10th Linked Data in Architecture and Construction Workshop (LDAC 2022)}, language = {eng}, number = 3213, pages = {43-52}, publisher = {RWTH Aachen}, series = {CEUR workshop proceedings}, title = {Knowledge Graphs for Multidisciplinary Co-Design : Introducing RDF to BHoM}, url = {https://ceur-ws.org/Vol-3213/}, venue = {Hersonissos, Greece}, year = 2022 }
Link
https://ceur-ws.org/Vol-3213/
22. D. Elshani, A. Lombardi, A. Fisher, S. Staab, D. Hernández, and T. Wortmann, “Inferential Reasoning in Co-Design Using Semantic Web Standards alongside BHoM,” in Proceedings of 33. Forum Bauinformatik, M. Slepicka, L. Kolbeck, S. Esser, K. Forth, F. Noichl, and J. Schlenger, Eds., Technische Universität München, 2022, pp. 89–97. doi: 10.14459/2022md1686600.
- BibTeX
BibTeX
@inproceedings{elshani2022inferential, address = {München}, author = {Elshani, Diellza and Lombardi, Alessio and Fisher, Al and Staab, Steffen and Hernández, Daniel and Wortmann, Thomas}, booktitle = {Proceedings of 33. Forum Bauinformatik}, doi = {10.14459/2022md1686600}, editor = {Slepicka, Martin and Kolbeck, Lothar and Esser, Sebastian and Forth, Kasimir and Noichl, Florian and Schlenger, Jonas}, eventdate = {2022-09-07/2022-09-09}, eventtitle = {33. Forum Bauinformatik}, language = {eng}, pages = {89-97}, publisher = {Technische Universität München}, title = {Inferential Reasoning in Co-Design Using Semantic Web Standards alongside BHoM}, venue = {München}, year = 2022 }
23. D. Hernández, L. Galárraga, and K. Hose, “Computing how-provenance for SPARQL queries via query rewriting,” Proceedings of the VLDB Endowment, vol. 14, Art. no. 13, 2021, doi: 10.14778/3484224.3484235.
- BibTeX
BibTeX
@article{hernandez2021computing, author = {Hernández, Daniel and Galárraga, Luis and Hose, Katja}, doi = {10.14778/3484224.3484235}, issn = {2150-8097}, journal = {Proceedings of the VLDB Endowment}, language = {eng}, number = 13, pages = {3389-3401}, publisher = {Association of Computing Machinery}, title = {Computing how-provenance for SPARQL queries via query rewriting}, volume = 14, year = 2021 }
24. D. Hernández, C. Gutierrez, and R. Angles, “The problem of correlation and substitution in SPARQL-Extended Version,” 2018. doi: https://doi.org/10.48550/arXiv.1801.04387.
- BibTeX
- Link
BibTeX
@techreport{Herńandez2018, author = {Hernández, Daniel and Gutierrez, Claudio and Angles, Renzo}, doi = {https://doi.org/10.48550/arXiv.1801.04387}, journal = {arXiv}, title = {The problem of correlation and substitution in SPARQL-Extended Version}, url = {http://arxiv.org/abs/1801.04387}, year = 2018 }
Link
http://arxiv.org/abs/1801.04387
25. D. Hernández, C. Gutiérrez, and A. Hogan, “Certain Answers for SPARQL with Blank Nodes,” in The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part I, D. Vrandecic, K. Bontcheva, M. C. Suárez-Figueroa, V. Presutti, I. Celino, M. Sabou, L.-A. Kaffee, and E. Simperl, Eds., in Lecture Notes in Computer Science, vol. 11136. Springer, 2018, pp. 337–353. doi: 10.1007/978-3-030-00671-6_20.
- BibTeX
- Link
BibTeX
@inproceedings{DBLP:conf/semweb/HernandezGH18, author = {Hernández, Daniel and Gutiérrez, Claudio and Hogan, Aidan}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part {I}}, doi = {10.1007/978-3-030-00671-6_20}, editor = {Vrandecic, Denny and Bontcheva, Kalina and Suárez-Figueroa, Mari Carmen and Presutti, Valentina and Celino, Irene and Sabou, Marta and Kaffee, Lucie{-}Aim{\'{e}}e and Simperl, Elena}, pages = {337--353}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Certain Answers for SPARQL with Blank Nodes}, url = {https://doi.org/10.1007/978-3-030-00671-6_20}, volume = 11136, year = 2018 }
Link
https://doi.org/10.1007/978-3-030-00671-6_20
26. D. Hernández and C. Gutiérrez, “Semantics for Querying Paths in Graph Databases: No-repeated-node or No-repeated-edge?,” in Proceedings of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web, Montevideo, Uruguay, June 7-9, 2017., J. L. Reutter and D. Srivastava, Eds., in CEUR Workshop Proceedings, vol. 1912. CEUR-WS.org, 2017. [Online]. Available: http://ceur-ws.org/Vol-1912/paper13.pdf
- BibTeX
- Link
BibTeX
@inproceedings{DBLP:conf/amw/HernandezG17, author = {Hernández, Daniel and Gutiérrez, Claudio}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web, Montevideo, Uruguay, June 7-9, 2017.}, editor = {Reutter, Juan L. and Srivastava, Divesh}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Semantics for Querying Paths in Graph Databases: No-repeated-node or No-repeated-edge?}, url = {http://ceur-ws.org/Vol-1912/paper13.pdf}, volume = 1912, year = 2017 }
Link
http://ceur-ws.org/Vol-1912/paper13.pdf
27. C. Gutiérrez, D. Hernández, A. Hogan, and A. Polleres, “Certain Answers for SPARQL?,” in Proceedings of the 10th Alberto Mendelzon International Workshop on Foundations of Data Management, Panama City, Panama, May 8-10, 2016., R. Pichler and A. S. da Silva, Eds., in CEUR Workshop Proceedings, vol. 1644. CEUR-WS.org, 2016. [Online]. Available: http://ceur-ws.org/Vol-1644/paper13.pdf
- BibTeX
- Link
BibTeX
@inproceedings{DBLP:conf/amw/GutierrezHHP16, author = {Gutiérrez, Claudio and Hernández, Daniel and Hogan, Aidan and Polleres, Axel}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 10th Alberto Mendelzon International Workshop on Foundations of Data Management, Panama City, Panama, May 8-10, 2016.}, editor = {Pichler, Reinhard and da Silva, Altigran Soares}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Certain Answers for SPARQL?}, url = {http://ceur-ws.org/Vol-1644/paper13.pdf}, volume = 1644, year = 2016 }
Link
http://ceur-ws.org/Vol-1644/paper13.pdf
28. D. Hernández, A. Hogan, C. Riveros, C. Rojas, and E. Zerega, “Querying Wikidata: Comparing SPARQL, Relational and Graph Databases,” in The Semantic Web - ISWC 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part II, P. T. Groth, E. Simperl, A. J. G. Gray, M. Sabou, M. Krötzsch, F. Lécué, F. Flöck, and Y. Gil, Eds., in Lecture Notes in Computer Science, vol. 9982. 2016, pp. 88–103. doi: 10.1007/978-3-319-46547-0_10.
- BibTeX
- Link
BibTeX
@inproceedings{DBLP:conf/semweb/HernandezHRRZ16, author = {Hernández, Daniel and Hogan, Aidan and Riveros, Cristian and Rojas, Carlos and Zerega, Enzo}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {The Semantic Web - {ISWC} 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part {II}}, doi = {10.1007/978-3-319-46547-0_10}, editor = {Groth, Paul T. and Simperl, Elena and Gray, Alasdair J. G. and Sabou, Marta and Kr{\"{o}}tzsch, Markus and L{\'{e}}cu{\'{e}}, Freddy and Fl{\"{o}}ck, Fabian and Gil, Yolanda}, pages = {88--103}, series = {Lecture Notes in Computer Science}, title = {Querying Wikidata: Comparing SPARQL, Relational and Graph Databases}, url = {https://doi.org/10.1007/978-3-319-46547-0_10}, volume = 9982, year = 2016 }
Link
https://doi.org/10.1007/978-3-319-46547-0_10
29. D. Hernández, A. Hogan, and M. Krötzsch, “Reifying RDF: What Works Well With Wikidata?,” in Proceedings of the 11th International Workshop on Scalable Semantic Web Knowledge Base Systems co-located with 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA, USA, October 11, 2015., T. Liebig and A. Fokoue, Eds., in CEUR Workshop Proceedings, vol. 1457. CEUR-WS.org, 2015, pp. 32–47. [Online]. Available: http://ceur-ws.org/Vol-1457/SSWS2015_paper3.pdf
- BibTeX
- Link
BibTeX
@inproceedings{DBLP:conf/semweb/HernandezHK15, author = {Hernández, Daniel and Hogan, Aidan and Krötzsch, Markus}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 11th International Workshop on Scalable Semantic Web Knowledge Base Systems co-located with 14th International Semantic Web Conference {(ISWC} 2015), Bethlehem, PA, USA, October 11, 2015.}, editor = {Liebig, Thorsten and Fokoue, Achille}, pages = {32--47}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Reifying RDF: What Works Well With Wikidata?}, url = {http://ceur-ws.org/Vol-1457/SSWS2015_paper3.pdf}, volume = 1457, year = 2015 }
Link
http://ceur-ws.org/Vol-1457/SSWS2015_paper3.pdf
30. D. Hernández and C. Gutiérrez, “Disentangling the Notion of Dataset in SPARQL,” in Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru, May 6 - 8, 2015., A. Cali and M.-E. Vidal, Eds., in CEUR Workshop Proceedings, vol. 1378. CEUR-WS.org, 2015. [Online]. Available: http://ceur-ws.org/Vol-1378/AMW_2015_paper_41.pdf
- BibTeX
- Link
BibTeX
@inproceedings{DBLP:conf/amw/HernandezG15, author = {Hernández, Daniel and Gutiérrez, Claudio}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru, May 6 - 8, 2015.}, editor = {Cali, Andrea and Vidal, Maria-Esther}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Disentangling the Notion of Dataset in {SPARQL}}, url = {http://ceur-ws.org/Vol-1378/AMW_2015_paper_41.pdf}, volume = 1378, year = 2015 }
Link
http://ceur-ws.org/Vol-1378/AMW_2015_paper_41.pdf

Teaching

University of Stuttgart

Human Computer Interaction with Knowledge Graphs, Lecturer (winter 2022).
Knowledge Graphs, Teaching Fellow (summer of 2022).

University of Aalborg

Semester Project, 4th Semester BSc Software Engineering, Group Supervisor (1st semester of 2021).

University of Chile

The Web of Data, Lecturer (2nd semester of 2012; 1st semester of 2014), Lecturer Assistant (2nd semester of 2015, 2016, and 2018).
Databases, Lecturer Assistant (2nd semester of 2012, and 1st semester of 2013).

University of Talca

Algorithmic Solution for Problems, Lecturer (1st and 2nd semesters of 2013).
Web Development, Lecturer (1st semester of 2013).
Databases, Lecturer (2nd semester of 2013).
Advanced Databases, Lecturer (2nd semester of 2013).

Vita

I am currently a postdoctoral researcher in the Analytic Computing department of the Institute for Artificial Intelligence at University of Stuttgart. Previously, I was a postdoctoral researcher in the Computer Science Department at Aalborg University and worked at Millennium Institute Foundational Research on Data in Chile. I hold a Ph.D. in Computer Science from the Department of Computer Science at the University of Chile.

Mastodon address: @daniel@mstdn.degu.cl

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Daniel Hernández

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University of Stuttgart

University of Aalborg

University of Chile

University of Talca

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