Demo Data from EDBT 2026 / reviewers in the wild / expert
Michael Vance
dblp:324/1778
· DBLP profile ↗
1ranked-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 2021Graphics, computer vision, multimedia, augmented reality and games · 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% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology › formal ontology
description logic ontology |
0.6 | 1 | 2022 | Modeling Abstract Algebra as an OWL Ontology (Student Abstract) · AAAI 2022 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology |
0.6 | 1 | 2022 | Modeling Abstract Algebra as an OWL Ontology (Student Abstract) · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
description logic · 0.6OWL · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Modeling Abstract Algebra as an OWL Ontology (Student Abstract)abstractDescription logic ontologies serve to model classifications and structural relationships, and to represent and reason about domain knowledge. Modeling the basic classification of abstract algebraic structures as an ontology demonstrates the difficulties presented by their logical semantics, and shed light on the limitations to accurately model further topics in algebra and related mathematical domains. Michael Vance |
AAAI | 1 |