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Gregory R. Olsen

dblp:22/3339 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1996
—ORCID · none

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

Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1Theory of computation · 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.

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

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology
0.021996
The configuration design ontologies and the VT elevator domain theory · Int. J. Hum. Comput. Stud. 1996
An Ontology for Engineering Mathematics · KR 1994
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology
domain ontology
0.011994
An Ontology for Engineering Mathematics · KR 1994
Knowledge, reasoning and agents › Knowledge representation and reasoning › domain knowledge
domain theory
0.011996
The configuration design ontologies and the VT elevator domain theory · Int. J. Hum. Comput. Stud. 1996

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

ontology engineering · 0.0
YearPublicationVenuePosition
1996 The configuration design ontologies and the VT elevator domain theory
abstract
In the VT/Sisyphus experiment, a set of problem solving systems were being built against a common specification of a problem. An important hypothesis was that the specification could be given, in large part, as a common ontology. This article is that ontology. This ontology is different than normal software specification documents in two fundamental ways. First, it is formal and machine readable (i.e. in the KIF/Ontolingua syntax). Second, the descriptions of the input and output of the task to be performed include domain knowledge (i.e. about elevator configuration) that characterize semantic constraints on possible solutions, rather than describing the form (data structure) of the answer. The article includes an overview of the conceptualization, excerpts from the machine-readable Ontolingua source files, and pointers to the complete ontology library available on the Internet.
Thomas R. Gruber, Gregory R. Olsen, Jay T. Runkel
Int. J. Hum. Comput. Stud.2
1994 An Ontology for Engineering Mathematics
Thomas R. Gruber, Gregory R. Olsen
KR2