Demo Data from EDBT 2026 / reviewers in the wild / expert
Atefe Khodadaditaghanaki
dblp:408/0257
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0000-4209-4787ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 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% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
datalog |
0.9 | 1 | 2025 | Inconsistency Handling in DatalogMTL · IJCAI 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › temporal reasoning
DatalogMTL |
0.9 | 1 | 2025 | Inconsistency Handling in DatalogMTL · IJCAI 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
inconsistency handling |
0.9 | 1 | 2025 | Inconsistency Handling in DatalogMTL · IJCAI 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic in computer science › logical foundations › non-classical logics › temporal logic
metric temporal logic |
0.9 | 1 | 2025 | Inconsistency Handling in DatalogMTL · IJCAI 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
temporal reasoning |
0.9 | 1 | 2025 | Inconsistency Handling in DatalogMTL · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
repair semantics · 0.9complexity analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Inconsistency Handling in DatalogMTLabstractIn this paper, we explore the issue of inconsistency handling in DatalogMTL, an extension of Datalog with metric temporal operators. Since facts are associated with time intervals, there are different manners to restore consistency when they contradict the rules, such as removing facts or modifying their time intervals. Our first contribution is the definition of relevant notions of conflicts (minimal explanations for inconsistency) and repairs (possible ways of restoring consistency) for this setting and the study of the properties of these notions and the associated inconsistency-tolerant semantics. Our second contribution is a data complexity analysis of the tasks of generating a single conflict / repair and query entailment under repair-based semantics. Meghyn Bienvenu, Camille Bourgaux, Atefe Khodadaditaghanaki |
IJCAI | 3 |