Demo Data from EDBT 2026 / reviewers in the wild / expert
Yehia Hatab
dblp:391/2556
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 first-author · 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% | |
| Theoretical computer science
1 paper |
Logic in computer science · 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 › inconsistency handling
inconsistency-tolerant reasoning |
0.9 | 1 | 2025 | A Framework for Inconsistency-tolerant Reasoning with Sets of Models · KR 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
nonmonotonic reasoning |
0.9 | 1 | 2025 | A Framework for Inconsistency-tolerant Reasoning with Sets of Models · KR 2025 |
Logic in computer science
many-valued logic |
0.9 | 1 | 2025 | A Framework for Inconsistency-tolerant Reasoning with Sets of Models · KR 2025 |
Methods — techniques the papers use, named apart from their topics
preference orders · 1.7minimal hitting sets · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Framework for Inconsistency-tolerant Reasoning with Sets of ModelsabstractWe propose a framework for reasoning from inconsistent knowledge bases using minimal hitting sets, i. e., sets of interpretations such that each formula of the knowledge base is satisfied by at least one those interpretations. By additionally considering preference orders over minimal hitting sets, we can define a wide variety of non-monotonic inference relations. We consider concrete preference orders based on set inclusion, cardinality, the number of conflicting atoms within the hitting set, and using the Hamming distance between pairs of interpretations. We compare the resulting inference relations, characterize their logical properties, and position them relative to classical inference from maximal consistent subsets. Finally, we show that inference based on minimal conflicting atoms coincides with reasoning in Priest’s 3-valued logic. Yehia Hatab, Kai Sauerwald, Matthias Thimm |
KR | 1 |