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Olivier Schmeltzer

dblp:07/1375 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 1995
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-authorArtificial intelligence and machine learning · 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.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 57% Computational science and engineering · 43%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics
0.011995
Using Temporal Reasoning for Genome Map Assembly · ISMB 1995
Computational science and engineering › scientific data management
knowledge base construction
0.011993
Building Large Knowledge Bases in Molecular Biology · ISMB 1993
Knowledge, reasoning and agents › Knowledge representation and reasoning
temporal reasoning
0.011995
Using Temporal Reasoning for Genome Map Assembly · ISMB 1995

Methods — techniques the papers use, named apart from their topics

temporal reasoning · 0.0knowledge representation · 0.0
YearPublicationVenuePosition
1995 Using Temporal Reasoning for Genome Map Assembly
Olivier Schmeltzer
ISMB1
1993 Integrating Constraints, Composite Objects and Tasks in a Knowledge Representation System
abstract
TROPES is an object-based knowledge representation system, containing three important intertwined features: constraints, composite objects and tasks. Constraints provide a declarative means to define and maintain relations between objects. The part-whole relation profits by constraints for sharing attribute values between a composite object and its components. The task model benefits from composite objects for task modeling and from classification and instantiation mechanisms for task execution; it also uses constraints to propagate input and output data from one task to another.
Jérôme Gensel, Pierre Girard, Olivier Schmeltzer
ICTAI3
1993 Building Large Knowledge Bases in Molecular Biology
Olivier Schmeltzer, Claudine Médigue, P. Uvietta, François Rechenmann, Frank Dorkeld, Guy Perrière, Christian Gautier
ISMB1