Demo Data from EDBT 2026 / reviewers in the wild / expert
Margot Flowers
dblp:63/4755
· DBLP profile ↗
13ranked-venue papers
1as first author
0since 2021 · last 1990
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 12 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 8 · 1 first-authorDatabases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 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
8 papers |
Knowledge representation and reasoning · 66% Information extraction and text analysis · 22% Learning theory · 12% | |
| Theoretical computer science
2 papers |
Logic in computer science · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 9 heaviest of 12, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
causal reasoning |
0.0 | 2 | 1987 | The Role of Prior Causal Theories in Generalization · AAAI 1986 Using Prior Learning to Facilitate the Learning of New Causal Theories · IJCAI 1987 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
qualitative reasoning |
0.0 | 1 | 1987 | Naive Mechanics Comprehension and Invention in EDISON · IJCAI 1987 |
Natural language and speech › Information extraction and text analysis
argument mining |
0.0 | 1 | 1986 | Editorial Comprehension in OpEd through Argument Units · AAAI 1986 |
Machine learning › Learning theory
generalization |
0.0 | 1 | 1986 | The Role of Prior Causal Theories in Generalization · AAAI 1986 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
argumentation |
0.0 | 2 | 1981 | Opportunistic Processing in Arguments · IJCAI 1981 Towards an AI Model of Argumentation · AAAI 1980 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
inconsistency handling |
0.0 | 1 | 1982 | On Being Contradictory · AAAI 1982 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge base
inconsistent knowledge bases |
0.0 | 1 | 1982 | On Being Contradictory · AAAI 1982 |
Logic in computer science › philosophical logic › non-classical logic
paraconsistent logic |
0.0 | 1 | 1982 | On Being Contradictory · AAAI 1982 |
Computational social science and digital humanities › computational linguistics
opinion mining |
0.0 | 1 | 1986 | Editorial Comprehension in OpEd through Argument Units · AAAI 1986 |
Methods — techniques the papers use, named apart from their topics
invention · 0.0comprehension · 0.0natural language understanding · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | Argument representation for editorial text
Sergio Alvarado, Michael G. Dyer, Margot Flowers |
Knowl. Based Syst. | 3 |
| 1990 | Argument comprehension and retrieval for editorial text
Sergio J. Alvarado, Michael G. Dyer, Margot Flowers |
Knowl. Based Syst. | 3 |
| 1988 | Recognizing and Responding to Plan-Oriented Misconceptions
Alex Quilici, Michael G. Dyer, Margot Flowers |
Comput. Linguistics | 3 |
| 1987 | Precedent-Based Legal Reasoning and Knowledge Acquisition in Contract Law: A Process ModelabstractIn the law, decisions in previous cases play a significant role in the presentation, understanding, and outcome of new cases. This is particularly true in the area of contract law where few statutes (explicit legal rules) exist. When presented with a new case, a lawyer must be able to identify important issues and make some predictions about how the case might be decided. The lawyer will often recall past cases which bear similarities to the current case and reason analogically to make these predictions. In order to perform these tasks, a lawyer must be able to remember past cases, organize them in memory so that cases that are conceptually similar are stored together (a lawyer normally won't be reminded of an irrelevant case), and make analogies between cases. Thus the organization and representation of knowledge in memory is crucial in building a model of lawyer's cognitive processes. This paper describes a process model, implemented in a computer program called STARE, which addresses these issues in the context of first-year law students learning contract law. Seth R. Goldman, Michael G. Dyer, Margot Flowers |
ICAIL | 3 |
| 1987 | Naive Mechanics Comprehension and Invention in EDISON
Michael G. Dyer, Margot Flowers, Jack Hodges |
IJCAI | 2 |
| 1987 | Using Prior Learning to Facilitate the Learning of New Causal Theories
Michael J. Pazzani, Michael G. Dyer, Margot Flowers |
IJCAI | 3 |
| 1986 | Editorial Comprehension in OpEd through Argument Units
Sergio Alvarado, Michael G. Dyer, Margot Flowers |
AAAI | 3 |
| 1986 | The Role of Prior Causal Theories in Generalization
Michael J. Pazzani, Michael G. Dyer, Margot Flowers |
AAAI | 3 |
| 1986 | EDISON: An engineering design invention system operating naively
Michael G. Dyer, Margot Flowers, Jack Hodges |
Artif. Intell. Eng. | 2 |
| 1985 | Learning to Understand Contractual Situations
Seth R. Goldman, Michael G. Dyer, Margot Flowers |
IJCAI | 3 |
| 1982 | On Being Contradictory
Margot Flowers |
AAAI | 1 |
| 1981 | Opportunistic Processing in Arguments
Rod McGuire, Lawrence Birnbaum, Margot Flowers |
IJCAI | 3 |
| 1980 | Towards an AI Model of Argumentation
Lawrence Birnbaum, Margot Flowers, Rod McGuire |
AAAI | 2 |