VLDB 2026 Research / reviewers in the wild / expert
Robert Rush
dblp:83/6679
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1997
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 first-author
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 |
Knowledge representation and reasoning · 50% Probabilistic and Bayesian machine learning · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › directed graphical model
influence diagrams |
0.0 | 1 | 1997 | Elecitation of Knowledge from Multiple Experts Using Network Inference · IEEE Trans. Knowl. Data Eng. 1997 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition
knowledge elicitation |
0.0 | 1 | 1997 | Elecitation of Knowledge from Multiple Experts Using Network Inference · IEEE Trans. Knowl. Data Eng. 1997 |
Methods — techniques the papers use, named apart from their topics
network inference · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Elecitation of Knowledge from Multiple Experts Using Network InferenceabstractEliciting knowledge from multiple experts usually entails the use of groups, and thus is subject to the problems inherent in group dynamics. We present a technique for multiple expert knowledge acquisition that does not rely upon the use of groups and can take advantage of technological advances in communications and computing, i.e., the Internet. The approach uses influence diagrams to represent the individual expert's understanding of the problem situation and develops a Multiple Expert Influence Diagram (MEID), a composite representation of the experts' knowledge. Following a review of present methods for multiple expert knowledge elicitation, we formally define the MEID, describe its manner of construction, and discuss its interpretation. We continue with a review of the issues to be faced in implementation of the technique, and give an illustrative example. Finally, we emphasize the need to provide users of decision aids with defensible measures of the quality of the rules produced by these aids. The MEID-approach is intended to serve as a first step in this direction. Robert Rush, William A. Wallace |
IEEE Trans. Knowl. Data Eng. | 1 |