Demo Data from VLDB 2026 / reviewers in the wild / expert
Daniele Fakhoury
dblp:242/5155
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2022
—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 2021Databases, data management, data science and information retrieval · 1
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 · 100% | |
| 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 graphs › knowledge graph construction
enterprise knowledge graph |
0.4 | 1 | 2019 | Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019 |
Knowledge graphs
knowledge graph construction |
0.4 | 1 | 2019 | Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019 |
Knowledge graphs
knowledge graph querying |
0.4 | 1 | 2019 | Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
datalog |
0.1 | 1 | 2019 | Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
logic-based reasoning |
0.1 | 1 | 2019 | Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | ExSpliNet: An interpretable and expressive spline-based neural network
Daniele Fakhoury, Emanuele Fakhoury, Hendrik Speleers |
Neural Networks | 1 |
| 2019 | Knowledge Graphs and Enterprise AI: The Promise of an Enabling TechnologyabstractAdopting a mature AI strategy is fundamental for modern knowledge companies to govern the proliferation of smart AI-driven applications and to coordinate them within coherent knowledge workflows. We propose knowledge graphs as the reference technology for the enterprise AI context, i.e., the complex of entities, properties and relationships that shape a business domain and constitute a common backbone for all AI-driven applications. We contribute and discuss principles to design software architectures for AI-driven applications based on knowledge graphs. We focus on the Vadalog system, a successful knowledge graph middleware from the University of Oxford and show knowledge graphs in action in a number of use cases from the financial domain. Luigi Bellomarini, Daniele Fakhoury, Georg Gottlob, Emanuel Sallinger |
ICDE | 2 |