Demo Data from EDBT 2026 / reviewers in the wild / expert
Marvin Grosser
dblp:414/9980
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Theory of computation · 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.
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% | |
| Theoretical computer science
1 paper |
Computational complexity · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
description logic |
0.9 | 1 | 2025 | Fitting Description Logic Ontologies to ABox and Query Examples · KR 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology-based query answering |
0.3 | 1 | 2025 | Fitting Description Logic Ontologies to ABox and Query Examples · KR 2025 |
Computational complexity › complexity of reasoning
description logic complexity |
0.3 | 1 | 2025 | Fitting Description Logic Ontologies to ABox and Query Examples · KR 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Fitting Horn DL Ontologies to ABox and Query Examples: A Tale of Simulation Quantifiers and Finite ModelsabstractWe study the problem of fitting a description logic (DL) ontology to a given set of positive and negative examples that take the form of an ABox and a Boolean query. While previous work has investigated this problem for the expressive DLs ALC and ALCI, we here focus on the Horn DLs EL and ELI, as well as their extensions with the bottom concept. As the query language, we consider atomic queries (AQs), conjunctive queries (CQs), and unions thereof (UCQs). We provide characterization of the existence of a fitting ontology based on simulations, use them to develop decision procedures, and clarify the exact computational complexity. For AQs, the problem is in PTime for both EL and ELI. For CQs and UCQ, it is Sigma_P^2-complete for EL and ExpTime-complete for ELI. Adding the bottom concept does not change any of these complexities. Interestingly, moving from ALC and ALCI to EL and ELI introduces additional technical challenges rather than simplifying the matter. Marvin Grosser, Carsten Lutz |
KR | 1 |
| 2025 | Fitting Description Logic Ontologies to ABox and Query ExamplesabstractWe study a fitting problem inspired by ontology-mediated querying: given a collection of positive and negative examples of the form (A, q) with A an ABox and q a query, we seek an ontology O such that A ∪ O entails q for all positive examples (A, q) and A ∪ O does not entail q for all negative examples (A, q). We consider the description logics ALC and ALCI as ontology languages and a range of query languages that includes atomic queries (AQs), conjunctive queries (CQs), and unions thereof (UCQs). For all of the resulting fitting problems, we provide effective characterizations and determine the computational complexity of deciding whether a fitting ontology exists. This problem turns out to be coNP-complete for AQs and full CQs and 2ExpTime-complete for CQs and UCQs. These results hold for both ALC and ALCI. Maurice Funk, Marvin Grosser, Carsten Lutz |
KR | 2 |