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Daniele Fakhoury

dblp:242/5155 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge graphs › knowledge graph construction
enterprise knowledge graph
0.412019
Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019
Knowledge graphs
knowledge graph construction
0.412019
Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019
Knowledge graphs
knowledge graph querying
0.412019
Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
datalog
0.112019
Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019
Knowledge, reasoning and agents › Knowledge representation and reasoning
logic-based reasoning
0.112019
Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology · ICDE 2019
YearPublicationVenuePosition
2022 ExSpliNet: An interpretable and expressive spline-based neural network
Daniele Fakhoury, Emanuele Fakhoury, Hendrik Speleers
Neural Networks1
2019 Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology
abstract
Adopting 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
ICDE2