Mathieu d’Aquin

4.1k total citations
136 papers, 1.6k citations indexed

About

Mathieu d’Aquin is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Mathieu d’Aquin has authored 136 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 101 papers in Artificial Intelligence, 57 papers in Information Systems and 34 papers in Molecular Biology. Recurrent topics in Mathieu d’Aquin's work include Semantic Web and Ontologies (91 papers), Service-Oriented Architecture and Web Services (35 papers) and Biomedical Text Mining and Ontologies (31 papers). Mathieu d’Aquin is often cited by papers focused on Semantic Web and Ontologies (91 papers), Service-Oriented Architecture and Web Services (35 papers) and Biomedical Text Mining and Ontologies (31 papers). Mathieu d’Aquin collaborates with scholars based in United Kingdom, Germany and Ireland. Mathieu d’Aquin's co-authors include Enrico Motta, Marta Sabou, Natalya F. Noy, Laurian Gridinoc, Sofia Angeletou, Stefan Dietze, Jean Lieber, Alessandro Adamou, Amedeo Napoli and Vanessa López and has published in prestigious journals such as Scientific Reports, IEEE Access and Neural Networks.

In The Last Decade

Mathieu d’Aquin

131 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mathieu d’Aquin United Kingdom 22 1.2k 690 349 270 246 136 1.6k
Marta Sabou Austria 26 1.4k 1.2× 939 1.4× 267 0.8× 295 1.1× 202 0.8× 112 1.9k
Peter Mika Spain 21 1.2k 1.1× 1.0k 1.5× 124 0.4× 390 1.4× 270 1.1× 87 2.0k
Denny Vrandečić Germany 13 1.9k 1.6× 568 0.8× 297 0.9× 152 0.6× 398 1.6× 30 2.4k
Alexandre Passant Ireland 21 851 0.7× 669 1.0× 107 0.3× 346 1.3× 153 0.6× 73 1.5k
Heiko Paulheim Germany 21 1.8k 1.5× 780 1.1× 304 0.9× 249 0.9× 564 2.3× 137 2.4k
Siegfried Handschuh Germany 24 1.9k 1.6× 1.2k 1.7× 257 0.7× 384 1.4× 321 1.3× 146 2.4k
Dan Brickley United Kingdom 15 1.2k 1.1× 955 1.4× 210 0.6× 515 1.9× 274 1.1× 28 1.9k
Ramanathan V. Guha United States 8 1.1k 1.0× 521 0.8× 175 0.5× 247 0.9× 188 0.8× 17 1.6k
Rinke Hoekstra Netherlands 16 1.3k 1.1× 697 1.0× 236 0.7× 301 1.1× 132 0.5× 59 1.7k
Hafedh Mili Canada 16 1.5k 1.3× 1.1k 1.6× 324 0.9× 360 1.3× 97 0.4× 99 2.3k

Countries citing papers authored by Mathieu d’Aquin

Since Specialization
Citations

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

Fields of papers citing papers by Mathieu d’Aquin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathieu d’Aquin. 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 Mathieu d’Aquin. The network helps show where Mathieu d’Aquin may publish in the future.

Co-authorship network of co-authors of Mathieu d’Aquin

This figure shows the co-authorship network connecting the top 25 collaborators of Mathieu d’Aquin. A scholar is included among the top collaborators of Mathieu d’Aquin 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 Mathieu d’Aquin. Mathieu d’Aquin 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.
d’Aquin, Mathieu. (2024). On the role of knowledge graphs in AI-based scientific discovery. Journal of Web Semantics. 84. 100854–100854.
2.
Garzón‐Orjuela, Nathaly, Doaa Amin, Lukasz Porwol, et al.. (2024). Design and architecture of the CARA infrastructure for visualising and benchmarking patient data from general practice. BMJ Health & Care Informatics. 31(1). e101059–e101059. 2 indexed citations
3.
Vellinga, Akke, Penny Mealy, Anthony Staines, et al.. (2021). Corona citizens’ science project-repeated surveys of the Irish response to COVID-19 and subsequent lockdown and restrictive measures. Irish Journal of Medical Science (1971 -). 191(2). 577–588. 1 indexed citations
4.
Adamou, Alessandro, et al.. (2018). Crowdsourcing Linked Data on listening experiences through reuse and enhancement of library data. International Journal on Digital Libraries. 20(1). 61–79. 12 indexed citations
5.
Daga, Enrico, Mathieu d’Aquin, Aldo Gangemi, et al.. (2017). Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges. Open Research Online (The Open University). 4 indexed citations
6.
Daga, Enrico, Mathieu d’Aquin, Aldo Gangemi, & Enrico Motta. (2015). Bottom-Up Ontology Construction with Contento. Open Research Online (The Open University). 1–15. 2 indexed citations
7.
d’Aquin, Mathieu & Stefan Dietze. (2014). Open Education: A Growing, High Impact Area for Linked Open Data.. ERCIM news/ERCIM news online edition. 2014. 3 indexed citations
8.
Adamou, Alessandro, et al.. (2014). LED: curated and crowdsourced linked data on music listening experiences. Open Research Online (The Open University). 93–96. 4 indexed citations
9.
Guy, Marieke, et al.. (2014). LinkedUp: Linking Open Data for Education. DSpace (Open University in the Netherlands). 3 indexed citations
10.
Tiddi, Ilaria, Mathieu d’Aquin, & Enrico Motta. (2013). Explaining clusters with inductive logic programming and linked data. Open Research Online (The Open University). 257–260. 1 indexed citations
11.
d’Aquin, Mathieu, et al.. (2013). Modeling and reasoning upon facebook privacy settings. International Semantic Web Conference. 141–144. 1 indexed citations
12.
d’Aquin, Mathieu, Gabriel Kronberger, & Mari Carmen Suárez-Figueroa. (2012). Combining data mining and ontology engineering to enrich ontologies and linked data. 19–24. 10 indexed citations
13.
d’Aquin, Mathieu, et al.. (2009). DOOR: towards a formalization of ontology relations. Open Research Online (The Open University). 13–20. 27 indexed citations
14.
d’Aquin, Mathieu, Marta Sabou, Enrico Motta, et al.. (2008). What can be done with the Semantic Web? An overview of Watson-based applications. Open Research Online (The Open University). 13 indexed citations
15.
d’Aquin, Mathieu, Marta Sabou, & Enrico Motta. (2008). Reusing knowledge from the semantic web with the Watson plugin. International Semantic Web Conference. 114–115. 6 indexed citations
16.
Sabou, Marta, Mathieu d’Aquin, & Enrico Motta. (2008). Relation discovery from the semantic web. International Semantic Web Conference. 124–125. 2 indexed citations
17.
Haase, Peter, et al.. (2008). The NeOn Ontology Engineering Toolkit. 42 indexed citations
18.
d’Aquin, Mathieu, et al.. (2008). Finding equivalent ontologies in Watson. Open Research Online (The Open University). 12–13. 1 indexed citations
19.
Gracia, Jorge, Vanessa López, Mathieu d’Aquin, et al.. (2007). Solving semantic ambiguity to improve semantic web based ontology matching. Open Research Online (The Open University). 1–12. 31 indexed citations
20.
Sabou, Marta, Mathieu d’Aquin, & Enrico Motta. (2006). Using the semantic web as background knowledge for ontology mapping. Open Research Online (The Open University). 1–12. 41 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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