Demo Data from EDBT 2026 / reviewers in the wild / expert
Robert Dionne
dblp:80/765
· DBLP profile ↗
6ranked-venue papers
2as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorDatabases, 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.
| Theoretical computer science
2 papers |
Logic in computer science · 100% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Transaction processing and concurrency control · 56% Database system architecture and tuning · 44% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 100% |
Topics — the 11 heaviest of 14, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Logic in computer science › knowledge representation and reasoning
description logic |
0.0 | 1 | 1993 | The Equivalence of Model-Theoretic and Structural Subsumption in Description Logics · IJCAI 1993 |
Logic in computer science › formal semantics
model-theoretic semantics |
0.0 | 1 | 1993 | The Equivalence of Model-Theoretic and Structural Subsumption in Description Logics · IJCAI 1993 |
Logic in computer science
model theory |
0.0 | 1 | 1993 | The Equivalence of Model-Theoretic and Structural Subsumption in Description Logics · IJCAI 1993 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology |
0.0 | 1 | 1992 | A Non-Well-Founded Approach to Terminological Cycles · AAAI 1992 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › description logic
terminological cycles |
0.0 | 1 | 1992 | A Non-Well-Founded Approach to Terminological Cycles · AAAI 1992 |
Database system architecture and tuning
knowledge base management system |
0.0 | 1 | 1991 | A Persistent Store for Large Shared Knowledge Bases · IEEE Trans. Knowl. Data Eng. 1991 |
Transaction processing and concurrency control › versioning
version storage |
0.0 | 1 | 1991 | A Persistent Store for Large Shared Knowledge Bases · IEEE Trans. Knowl. Data Eng. 1991 |
Storage systems › storage architecture
persistent storage |
0.0 | 1 | 1991 | A Persistent Store for Large Shared Knowledge Bases · IEEE Trans. Knowl. Data Eng. 1991 |
Storage systems › storage management
versioned storage |
0.0 | 1 | 1991 | A Persistent Store for Large Shared Knowledge Bases · IEEE Trans. Knowl. Data Eng. 1991 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
description logic |
0.0 | 1 | 1992 | A Non-Well-Founded Approach to Terminological Cycles · AAAI 1992 |
Transaction processing and concurrency control › update management
concurrent update |
0.0 | 1 | 1991 | A Persistent Store for Large Shared Knowledge Bases · IEEE Trans. Knowl. Data Eng. 1991 |
Methods — techniques the papers use, named apart from their topics
non-well-founded set theory · 0.0versioning · 0.0clustering · 0.0equivalence proof · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Modeling a description logic vocabulary for cancer researchabstractThe National Cancer Institute has developed the NCI Thesaurus, a biomedical vocabulary for cancer research, covering terminology across a wide range of cancer research domains. A major design goal of the NCI Thesaurus is to facilitate translational research. We describe: the features of Ontylog, a description logic used to build NCI Thesaurus; our methodology for enhancing the terminology through collaboration between ontologists and domain experts, and for addressing certain real world challenges arising in modeling the Thesaurus; and finally, we describe the conversion of NCI Thesaurus from Ontylog into Web Ontology Language Lite. Ontylog has proven well suited for constructing big biomedical vocabularies. We have capitalized on the Ontylog constructs Kind and Role in the collaboration process described in this paper to facilitate communication between ontologists and domain experts. The artifacts and processes developed by NCI for collaboration may be useful in other biomedical terminology development efforts. Frank W. Hartel, Sherri de Coronado, Robert Dionne, Gilberto Fragoso, Jennifer Golbeck |
J. Biomed. Informatics | 3 |
| 2003 | Enhancing Quality of Retrieval Through Concept Edit History
Francis W. Hartel, Gilberto Fragoso, Kim L. Ong, Robert Dionne |
AMIA | 4 |
| 2002 | Role grouping as an extension to the description logic of Ontylog, motivated by concept modeling in SNOMED
Kent A. Spackman, Robert Dionne, Eric Mays, Jason Weis |
AMIA | 2 |
| 1993 | The Equivalence of Model-Theoretic and Structural Subsumption in Description Logics
Robert Dionne, Eric Mays, Frank J. Oles |
IJCAI | 1 |
| 1992 | A Non-Well-Founded Approach to Terminological Cycles
Robert Dionne, Eric Mays, Frank J. Oles |
AAAI | 1 |
| 1991 | A Persistent Store for Large Shared Knowledge BasesabstractA knowledge-base management system (KBMS) with the following goals is proposed: (1) to allow a knowledge engineer to update a knowledge base and have these updates persist on secondary storage, (2) to allow multiple knowledge engineers to have shared access to a knowledge base and modify the knowledge base concurrently, and (3) to maintain consistency of the shared knowledge base as it evolves. At the heart of the KBMS is the version store, which is a persistent storage structure that maintains multiple versions of a knowledge base. Retrieve and update operations are defined on the version store to efficiently access and modify any version. Objects in the version store are clustered to support efficient access of an entire version or some subparts of it. To store the effects of an update requires space proportional to the size of the update and which is independent of the size of the knowledge base. The additional cost of maintaining multiple versions is within a small constant factor of maintaining a single version.> Eric Mays, Sitaram Lanka, Robert Dionne, Robert A. Weida |
IEEE Trans. Knowl. Data Eng. | 3 |