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William C. Ogden

dblp:78/6067 · DBLP profile ↗
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5ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

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

Databases, data management, data science and information retrieval · 2Human-computer interaction and ubiquitous computing · 2 · 1 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.

Databases, data mining, and information retrieval
3 papers
Information retrieval · 86% Data models and query languages · 10% Query processing and optimization · 4%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%
Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
cross-language information retrieval
0.011997
QUILT: Implementing a Large-scale Cross-language Text Retrieval System · SIGIR 1997
Information retrieval
indexing
0.011997
QUILT: Implementing a Large-scale Cross-language Text Retrieval System · SIGIR 1997
Computing education › computer science curriculum
database education
0.011986
A cognitive model of database querying: a tool for novice instruction · CHI 1986
Usability and user experience research › cognitive modeling
cognitive models of interaction
0.011986
A cognitive model of database querying: a tool for novice instruction · CHI 1986

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

machine translation · 0.0cognitive modeling · 0.0taxonomy of query languages · 0.0
YearPublicationVenuePosition
2000 Towards Universal Text Retrieval: Tipster Text Retrieval Research at New Mexico State University
Mark W. Davis, William C. Ogden
Inf. Retr.2
1997 QUILT: Implementing a Large-scale Cross-language Text Retrieval System
abstract
Ahtract Q~T (QWXY Uaa Interfacewith Light Tmnaladon8)k a prototype implementadon of a completecmas-langusge textretrieval 8ystemtbat takeaFkgli8hqude3snd pmduceaw811glo8a translations of Spani8hdocumenla.The ayatcmindexesthe Spm- iah(kmmnts inspatda but-*
Mark W. Davis, William C. Ogden
SIGIR2
1995 Text Alignment in the Real World: Improving Alignments of Noisy Translations Using Common Lexical Features, String Matching Strategies and N-Gram Comparisons
Mark W. Davis, Ted Dunning, William C. Ogden
EACL3
1986 A cognitive model of database querying: a tool for novice instruction
Mark S. Schlager, William C. Ogden
CHI2
1983 Query languages for the casual user: Exploring the middle ground between formal and natural languages
abstract
In the past the non-programmer who wanted the information contained in a computer database had to employ an expert programmer knowledgeable in the language and structure of the database. Now languages are being developed that are designed to be used by an infrequent or “casual” user who has limited knowledge of how the data is stored or retrieved by the computer. These special purpose query languages which allow these casual users to retrieve information from computer databases are commonly referred to as “nonprocedural” (Leavenworth and Sammet, 1974) because users need only describe the data to be retrieved, not how it is to be retrieved. These languages can be classified into two basic types which are characterized by the level of constraint imposed on the syntax and vocabulary of the language (Ehrenreich, 1981). Formal query languages have a very constrained syntax and vocabulary, while natural query languages have a relatively unconstrained syntax and vocabulary.
William C. Ogden, Susan R. Brooks
CHI1