Demo Data from EDBT 2026 / reviewers in the wild / expert
Bianca Loehnert
dblp:304/1148 · also Bianca Löhnert
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 50% Data models and query languages · 50% | |
| Theoretical computer science
1 paper |
Logic in computer science · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data models and query languages › datalog › datalog query optimization
magic sets |
0.6 | 1 | 2022 | Magic Shapes for SHACL Validation · Proc. VLDB Endow. 2022 |
Knowledge graphs › knowledge graph validation
SHACL validation |
0.6 | 1 | 2022 | Magic Shapes for SHACL Validation · Proc. VLDB Endow. 2022 |
Logic in computer science
logic programming |
0.2 | 1 | 2022 | Magic Shapes for SHACL Validation · Proc. VLDB Endow. 2022 |
Logic in computer science › logic programming
magic sets |
0.2 | 1 | 2022 | Magic Shapes for SHACL Validation · Proc. VLDB Endow. 2022 |
Methods — techniques the papers use, named apart from their topics
recursion · 1.1negation · 1.1magic sets · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Towards Practicable Algorithms for Rewriting Graph Queries Beyond DL-Lite
Bianca Loehnert, Nikolaus Augsten, Cem Okulmus, Magdalena Ortiz 0001 |
ESWC (1) | 1 |
| 2022 | Magic Shapes for SHACL ValidationabstractA key prerequisite for the successful adoption of the Shapes Constraint Language (SHACL)---the W3C standardized constraint language for RDF graphs---is the availability of automated tools that efficiently validate targeted constraints (known as shapes graphs ) over possibly very large RDF graphs. There are already significant efforts to produce optimized engines for SHACL validation, but they focus on restricted fragments of SHACL. For unrestricted SHACL, that is SHACL with unrestricted recursion and negation, there is no validator beyond a proof-of-concept prototype, and existing techniques are inherently incompatible with the goal-driven approaches being pursued by existing validators. Instead they require a global computation on the entire data graph that is not only computationally very costly, but also brittle, and can easily result in validation failures due to conflicts that are irrelevant to the validation targets. To address these challenges, we present a 'magic' transformation---based on Magic Sets as known from Logic Programming---that transforms a SHACL shapes graph S into a new shapes graph S' whose validation considers only the relevant neighbourhood of the targeted nodes. The new S' is equivalent to S whenever there are no conflicts between the constraints and the data, and in case the validation of S fails due to conflicts that are irrelevant to the target, S' may still admit a lazy, target-oriented validation. We implement the algorithm and run preliminary experiments, showing our approach can be a stepping stone towards validators for full SHACL, and that it can significantly improve the performance of the only prototype validator that currently supports full recursion and negation. Shqiponja Ahmetaj, Bianca Loehnert, Magdalena Ortiz 0001, Mantas Simkus |
Proc. VLDB Endow. | 2 |