Mounia Lalmas

8.3k total citations
216 papers, 3.8k citations indexed

About

Mounia Lalmas is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Mounia Lalmas has authored 216 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 118 papers in Information Systems, 95 papers in Artificial Intelligence and 44 papers in Computer Networks and Communications. Recurrent topics in Mounia Lalmas's work include Information Retrieval and Search Behavior (61 papers), Semantic Web and Ontologies (53 papers) and Web Data Mining and Analysis (37 papers). Mounia Lalmas is often cited by papers focused on Information Retrieval and Search Behavior (61 papers), Semantic Web and Ontologies (53 papers) and Web Data Mining and Analysis (37 papers). Mounia Lalmas collaborates with scholars based in United Kingdom, Spain and United States. Mounia Lalmas's co-authors include Ian Ruthven, Rishabh Mehrotra, Tobias Blanke, Benjamin Piwowarski, Gabriella Kazai, Andrew Trotman, Norbert Fuhr, Cornelis J. van Rijsbergen, Elad Yom‐Tov and Fábio Crestani and has published in prestigious journals such as ACM Computing Surveys, Information Processing & Management and ACM SIGMOD Record.

In The Last Decade

Mounia Lalmas

207 papers receiving 3.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mounia Lalmas United Kingdom 33 1.8k 1.7k 623 527 527 216 3.8k
Joemon M. Jose United Kingdom 32 2.5k 1.4× 1.9k 1.1× 399 0.6× 1.2k 2.2× 370 0.7× 235 4.3k
James Caverlee United States 33 2.5k 1.4× 2.1k 1.3× 734 1.2× 418 0.8× 520 1.0× 166 4.6k
Nigel Shadbolt United Kingdom 37 1.9k 1.0× 2.6k 1.6× 989 1.6× 430 0.8× 239 0.5× 270 5.6k
Fábio Crestani Switzerland 27 1.6k 0.9× 2.0k 1.2× 441 0.7× 409 0.8× 422 0.8× 238 3.3k
Yi Chang United States 35 1.8k 1.0× 3.7k 2.2× 371 0.6× 767 1.5× 465 0.9× 201 5.7k
Marcos André Gonçalves Brazil 38 2.3k 1.3× 3.2k 1.9× 800 1.3× 757 1.4× 286 0.5× 313 5.8k
Tefko Saračević United States 32 4.2k 2.4× 2.0k 1.2× 616 1.0× 454 0.9× 497 0.9× 97 6.1k
Mark Sanderson Australia 35 2.7k 1.5× 2.9k 1.7× 303 0.5× 764 1.4× 708 1.3× 254 5.2k
Pearl Pu Switzerland 33 1.9k 1.1× 1.6k 0.9× 716 1.1× 693 1.3× 458 0.9× 146 3.8k
Li Chen Hong Kong 38 3.0k 1.7× 2.7k 1.6× 996 1.6× 798 1.5× 387 0.7× 262 5.7k

Countries citing papers authored by Mounia Lalmas

Since Specialization
Citations

This map shows the geographic impact of Mounia Lalmas'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 Mounia Lalmas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mounia Lalmas more than expected).

Fields of papers citing papers by Mounia Lalmas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mounia Lalmas. 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 Mounia Lalmas. The network helps show where Mounia Lalmas may publish in the future.

Co-authorship network of co-authors of Mounia Lalmas

This figure shows the co-authorship network connecting the top 25 collaborators of Mounia Lalmas. A scholar is included among the top collaborators of Mounia Lalmas 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 Mounia Lalmas. Mounia Lalmas 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.
Palla, Konstantina, et al.. (2025). Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models. 840–854. 1 indexed citations
2.
Nadai, Marco De, Alice Wang, Fabrizio Silvestri, et al.. (2024). Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks. 403–412. 7 indexed citations
3.
Saito, Yuta, et al.. (2024). Long-term Off-Policy Evaluation and Learning. 3432–3443. 1 indexed citations
4.
Abdollahpouri, Himan, et al.. (2023). Calibrated Recommendations as a Minimum-Cost Flow Problem. 571–579. 8 indexed citations
5.
Palumbo, Enrico, Andreas Damianou, Alice Wang, et al.. (2023). Graph Learning for Exploratory Query Suggestions in an Instant Search System. IRIS Research product catalog (Sapienza University of Rome). 4780–4786. 1 indexed citations
6.
Maystre, Lucas, et al.. (2021). Collaborative Classification from Noisy Labels. International Conference on Artificial Intelligence and Statistics. 1639–1647.
7.
Song, Dawei, Mounia Lalmas, C. J. van Rijsbergen, et al.. (2010). How quantum theory is developing the field of Information Retrieval. Open Research Online (The Open University). 105–108. 8 indexed citations
8.
Borlund, Pia, Jesper Wiborg Schneider, Mounia Lalmas, et al.. (2008). Proceedings of the second international symposium on Information interaction in context. 4 indexed citations
9.
Fuhr, Norbert, Jaap Kamps, Mounia Lalmas, & Andrew Trotman. (2008). Focused Access to XML Documents: 6th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2007, Schloss Dagstuhl, Germany. Springer eBooks. 2 indexed citations
10.
Lalmas, Mounia, et al.. (2007). Using XML logical structure to retrieve (multimedia). 100–111. 3 indexed citations
11.
Fuhr, Norbert, Mounia Lalmas, Saadia Malik, & Gabriella Kazai. (2006). Advances in XML Information Retrieval and Evaluation: 4th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2005, Dagstuhl ... Papers (Lecture Notes in Computer Science). Springer eBooks. 25 indexed citations
12.
Lalmas, Mounia, et al.. (2006). Advances in Information Retrieval: 28th European Conference on IR Research, ECIR 2006, London, UK, April 10-12, 2006, Proceedings (Lecture Notes in Computer Science). Springer eBooks. 2 indexed citations
14.
Fuhr, Norbert, Mounia Lalmas, Saadia Malik, & Zoltán Szlávik. (2005). Advances in XML Information Retrieval: Third International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2004, Dagstuhl Castle, ... 2004 (Lecture Notes in Computer Science). Springer eBooks. 16 indexed citations
15.
Kazai, Gabriella, et al.. (2004). Searching Annotated Broadcast Content on Mobile and Stationary Devices.
16.
Hertzum, Morten, Mounia Lalmas, & Erik Frøkjær. (2001). How Are Searching and Reading Intertwined during Retrieval from Hierarchically Structured Documents. International Conference on Human-Computer Interaction. 537–544. 5 indexed citations
17.
Lalmas, Mounia, et al.. (2000). A Dempster-Shafer indexing for the focussed retrieval of a hierarchically structured document space: implementation and experiments on a web museum collection. 442–456. 14 indexed citations
18.
Lalmas, Mounia & Cornelis J. van Rijsbergen. (1998). Information Retrieval: Uncertainty and Logics: Advanced Models for the Representation and Retrieval of Information. Kluwer Academic Publishers eBooks. 35 indexed citations
19.
Lalmas, Mounia. (1997). Dempster-Shafer's theory of evidence applied to structured documents. ACM SIGIR Forum. 31(SI). 110–118. 10 indexed citations
20.
Crestani, Fábio, et al.. (1997). Short Queries, Natural Language and Spoken Document Retrieval: Experiments at Glasgow University.. Text REtrieval Conference. 667–686. 16 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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