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EDBT 2026 Demo / reviewers in the wild / expert

Ross Taylor

dblp:19/675 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 1Software engineering, systems software and programming languages · 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
1 paper
Information extraction and text analysis · 100%
Databases, data mining, and information retrieval
1 paper
Knowledge graphs · 100%

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

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › document analysis › scholarly text analysis
scientific information extraction		 0.412020
AxCell: Automatic Extraction of Results from Machine Learning Papers · EMNLP (1) 2020
Knowledge graphs › knowledge graph construction › knowledge extraction
scientific knowledge extraction		 0.412020
AxCell: Automatic Extraction of Results from Machine Learning Papers · EMNLP (1) 2020
Software testing
partition testing		 0.011990
Partition Testing Does Not Inspire Confidence · IEEE Trans. Software Eng. 1990

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

theoretical modeling · 0.0
YearPublicationVenuePosition
2020 AxCell: Automatic Extraction of Results from Machine Learning Papers
abstract
Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, Robert Stojnic. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel 0001, Ross Taylor, Robert Stojnic
 EMNLP (1)6
1990 Partition Testing Does Not Inspire Confidence
abstract
Theoretical models are used to study partition testing in the abstract and to describe the circumstances under which it should perform well at failure detection. Partition testing is shown to be more valuable when the partitions are narrowly based on expected failures and there is a good chance that failures occur. It is concluded that for gaining confidence from successful tests, partition testing as usually practiced has little value.>
Richard G. Hamlet, Ross Taylor
 IEEE Trans. Software Eng.2