Maya Sappelli

985 citations
32 papers · 577 · h-index 9

Impact in

Papers in

Maya Sappelli

32 papers receiving 557 citations

Peers

Maya Sappelli
Comparison fields: 5 of 68
  • Developmental and Educational Psychology 179
  • Cognitive Neuroscience 229
  • Experimental and Cognitive Psychology 139
  • Information Systems and Management 46
  • Artificial Intelligence 145
Replace Krysta Chauncey with:
Krysta Chauncey United States
Hana Vrzáková Finland
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Patrick Gebhard Germany
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Amy Witherspoon United States
Ionuț Damian Germany
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Citations per field
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Citations per year

Countries citing papers authored by Maya Sappelli

Since Specialization
Citations

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

Fields of papers citing papers by Maya Sappelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Maya Sappelli, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Maya Sappelli Line = papers co-authored together Maya Sappelli links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010221
2 2014162
3 201627
4 201622
5 201318
6 201412
7 201311
8 201610
9 201510
10 20208
11
Term extraction for user profiling: evaluation by the user
20138
12
Ontologies for Social, Cognitive and Affective Agent-Based Support of Child’s Diabetes Self Management
20167
13
Query Interpretation – an Application of Semiotics in Image Retrieval
20157
14 20196
15 20125
16
Combining textual and non-textual features for e-mail importance estimation
20135
17 20155
18 20175
19 20144
20
User Privacy in Applications for Well-being and Well-working : Requirements and Approaches for User Controlled Privacy
20124

About Maya Sappelli

Maya Sappelli is a scholar working on Artificial Intelligence, Information Systems and Management, Information Systems, Computer Vision and Pattern Recognition and Molecular Biology, having authored 32 papers that have together received 577 indexed citations. Recurring topics across this work include Personal Information Management and User Behavior (10 papers), Advanced Text Analysis Techniques (8 papers), Topic Modeling (4 papers), Recommender Systems and Techniques (4 papers), Semantic Web and Ontologies (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Context-Aware Activity Recognition Systems (3 papers) and Information Retrieval and Search Behavior (3 papers). The work is most often cited by research in Developmental and Educational Psychology (179 citations), Cognitive Neuroscience (229 citations), Experimental and Cognitive Psychology (139 citations), Information Systems and Management (46 citations) and Artificial Intelligence (145 citations). Maya Sappelli has collaborated with scholars based in Netherlands, Germany and Italy. Frequent co-authors include Wessel Kraaij, Suzan Verberne, Ton Dijkstra, Koji Miwa, R. Harald Baayen, Saskia Koldijk, Mark A. Neerincx, Maaike de Boer, Djoerd Hiemstra and Gabriella Pasi. Their work appears in journals such as Journal of Memory and Language, Journal of the Association for Information Science and Technology, ACM SIGIR Forum, Information Sciences and ACM Transactions on Interactive Intelligent Systems.

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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