Demo Data from VLDB 2026 / reviewers in the wild / expert
Hebatalla Hammad
dblp:356/4573
· DBLP profile ↗
1ranked-venue papers
0as 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 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.
| Databases, data mining, and information retrieval
1 paper |
Query processing and optimization · 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 |
|---|---|---|---|---|
Query processing and optimization
datalog rewriting |
0.9 | 1 | 2025 | A Datalog Rewriting Algorithm for Warded Ontologies · IJCAI 2025 |
Query processing and optimization › semantic query processing
ontology-based query answering |
0.9 | 1 | 2025 | A Datalog Rewriting Algorithm for Warded Ontologies · IJCAI 2025 |
Logic in computer science › knowledge representation and reasoning
existential rules |
0.3 | 1 | 2025 | A Datalog Rewriting Algorithm for Warded Ontologies · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
datalog rewriting · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Datalog Rewriting Algorithm for Warded OntologiesabstractExistential rules, a.k.a. tuple-generating dependencies (TGDs), form a well-established formalism for specifying ontologies. In particular, the warded language is a well-behaved fragment of TGD-based ontologies, striking a good balance between expressive power and computational complexity of answering Ontology-Mediated Queries (OMQs). The theoretical foundations of answering OMQs over warded ontologies are by now well-understood, but to the best of our knowledge, very few efforts exist that exploit such a rich theory for building practical query answering algorithms. Our goal is to fill the above gap by designing a novel Datalog rewriting algorithm for OMQs over warded ontologies which is amenable to practical implementations, as well as providing an implementation and an experimental evaluation, with the aim of understanding how key input parameters affect the performance of this approach, and what are its limits when combined with off-the-shelf Datalog-based engines. Davide Benedetto, Marco Calautti, Hebatalla Hammad, Emanuel Sallinger, Adriano Vlad-Starrabba |
IJCAI | 3 |