Marie-Laure Mugnier

2.6k total citations
50 papers, 879 citations indexed

About

Marie-Laure Mugnier is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Marie-Laure Mugnier has authored 50 papers receiving a total of 879 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 33 papers in Computer Networks and Communications and 11 papers in Signal Processing. Recurrent topics in Marie-Laure Mugnier's work include Semantic Web and Ontologies (38 papers), Advanced Database Systems and Queries (25 papers) and Logic, Reasoning, and Knowledge (13 papers). Marie-Laure Mugnier is often cited by papers focused on Semantic Web and Ontologies (38 papers), Advanced Database Systems and Queries (25 papers) and Logic, Reasoning, and Knowledge (13 papers). Marie-Laure Mugnier collaborates with scholars based in France, Germany and Switzerland. Marie-Laure Mugnier's co-authors include Michel Chein, Jean-François Baget, Michel Leclère, Éric Salvat, Michaël Thomazo, Sebastian Rudolph, Michel Habib, Marianne Huchard, Gerd Stumme and Frithjof Dau and has published in prestigious journals such as Expert Systems with Applications, Artificial Intelligence and Computers and Electronics in Agriculture.

In The Last Decade

Marie-Laure Mugnier

45 papers receiving 790 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marie-Laure Mugnier France 15 692 390 160 156 84 50 879
Joachim W. Schmidt Germany 16 518 0.7× 653 1.7× 309 1.9× 294 1.9× 48 0.6× 70 1.0k
Birte Glimm Germany 15 749 1.1× 228 0.6× 311 1.9× 54 0.3× 65 0.8× 74 911
Larry Kerschberg United States 20 730 1.1× 686 1.8× 556 3.5× 381 2.4× 107 1.3× 101 1.3k
Misael Mongiovı̀ Italy 15 339 0.5× 165 0.4× 118 0.7× 93 0.6× 33 0.4× 43 687
Kathleen Fisher United States 21 799 1.2× 504 1.3× 438 2.7× 173 1.1× 64 0.8× 78 1.4k
Katsumi Inoue Japan 18 960 1.4× 246 0.6× 89 0.6× 51 0.3× 54 0.6× 166 1.2k
Jim Webber United Kingdom 8 283 0.4× 323 0.8× 271 1.7× 78 0.5× 45 0.5× 15 730
Marina Mongiello Italy 18 429 0.6× 289 0.7× 438 2.7× 47 0.3× 44 0.5× 87 860
Rajshekhar Sunderraman United States 12 213 0.3× 214 0.5× 154 1.0× 116 0.7× 69 0.8× 96 527
Valerie Cross United States 13 420 0.6× 99 0.3× 154 1.0× 149 1.0× 177 2.1× 79 674

Countries citing papers authored by Marie-Laure Mugnier

Since Specialization
Citations

This map shows the geographic impact of Marie-Laure Mugnier's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marie-Laure Mugnier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marie-Laure Mugnier more than expected).

Fields of papers citing papers by Marie-Laure Mugnier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marie-Laure Mugnier. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marie-Laure Mugnier. The network helps show where Marie-Laure Mugnier may publish in the future.

Co-authorship network of co-authors of Marie-Laure Mugnier

This figure shows the co-authorship network connecting the top 25 collaborators of Marie-Laure Mugnier. A scholar is included among the top collaborators of Marie-Laure Mugnier based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marie-Laure Mugnier. Marie-Laure Mugnier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Najm, Elie, et al.. (2024). Integrating data and knowledge to support the selection of service plant species in agroecology. Computers and Electronics in Agriculture. 217. 108594–108594. 2 indexed citations
2.
Mugnier, Marie-Laure, et al.. (2023). Scalable Reasoning on Document Stores via Instance-Aware Query Rewriting. Proceedings of the VLDB Endowment. 16(11). 2699–2713.
3.
Leclère, Michel, et al.. (2023). Query Rewriting with Disjunctive Existential Rules and Mappings. SPIRE - Sciences Po Institutional REpository. 429–439. 1 indexed citations
4.
Carral, David, et al.. (2022). Normalisations of Existential Rules: Not so Innocuous!. HAL (Le Centre pour la Communication Scientifique Directe). 102–111. 1 indexed citations
5.
Leclère, Michel, et al.. (2016). On Bounded Positive Existential Rules. HAL (Le Centre pour la Communication Scientifique Directe). 5 indexed citations
6.
Leclère, Michel, et al.. (2015). Query rewriting for existential rules with compiled preorder. HAL (Le Centre pour la Communication Scientifique Directe). 3106–3112. 4 indexed citations
7.
Chein, Michel, Marie-Laure Mugnier, & Madalina Croitoru. (2013). Visual reasoning with graph-based mechanisms: the good, the better and the best. The Knowledge Engineering Review. 28(3). 249–271. 13 indexed citations
8.
Mugnier, Marie-Laure, et al.. (2012). On the complexity of entailment in existential conjunctive first-order logic with atomic negation. Information and Computation. 215. 8–31. 6 indexed citations
9.
Baget, Jean-François, Michel Leclère, Marie-Laure Mugnier, & Éric Salvat. (2011). On rules with existential variables: Walking the decidability line. Artificial Intelligence. 175(9-10). 1620–1654. 132 indexed citations
10.
Baget, Jean-François, et al.. (2010). Logical, graph based knowledge representation with CoGui. HAL (Le Centre pour la Communication Scientifique Directe). 15–25. 3 indexed citations
11.
Baget, Jean-François, Michel Leclère, Marie-Laure Mugnier, & Éric Salvat. (2008). DL-SR: a Lite DL with Expressive Rules: Preliminary Results. HAL (Le Centre pour la Communication Scientifique Directe). 11. 3 indexed citations
12.
Leclère, Michel, et al.. (2007). Introducing reasoning into an industrial knowledge management tool. Applied Intelligence. 31(3). 211–224. 11 indexed citations
13.
Mugnier, Marie-Laure & Michel Leclère. (2006). On querying simple conceptual graphs with negation. Data & Knowledge Engineering. 60(3). 468–493. 6 indexed citations
14.
Moor, Aldo de, Frithjof Dau, Marie-Laure Mugnier, & Gerd Stumme. (2005). Patterns for the Pragmatic Web. VUBIR (Vrije Universiteit Brussel). 22 indexed citations
15.
Chein, Michel & Marie-Laure Mugnier. (2005). A Graph-Based Approach to Knowledge Representation: Computational Foundations of Conceptual Graphs (Part. I). HAL (Le Centre pour la Communication Scientifique Directe).
16.
Baget, Jean-François & Marie-Laure Mugnier. (2001). The SG family: extensions of simple conceptual graphs. International Joint Conference on Artificial Intelligence. 205–210. 5 indexed citations
17.
Baget, Jean-François, et al.. (1999). Knowledge Acquisition with a Pure Graph-Based Knowledge Representation Model - Application to the Sisyphus-I Case Study. 2 indexed citations
18.
Chein, Michel, et al.. (1998). Nested Graphs: A Graph-based Knowledge Representation Model with FOL Semantics.. Principles of Knowledge Representation and Reasoning. 524–535. 16 indexed citations
19.
Chein, Michel & Marie-Laure Mugnier. (1992). Conceptual graphs: fundamental notions. Revue d intelligence artificielle. 6(4). 365–406. 88 indexed citations
20.
Habib, Michel, et al.. (1992). Monotonic conflict resolution mechanisms for inheritance. ACM SIGPLAN Notices. 27(10). 16–24. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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