Daniel Campos

2.2k citations
12 papers · 130 · h-index 5

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Speech and dialogue systems
    • Information Retrieval and Search Behavior
    • Recommender Systems and Techniques
    • Expert finding and Q&A systems
    • Web Data Mining and Analysis

Papers in

Daniel Campos

9 papers receiving 128 citations

Peers

Daniel Campos
Comparison fields: 5 of 29
  • Artificial Intelligence 110
  • Information Systems 58
  • Health Informatics 3
  • Management Science and Operations Research 14
  • Computer Vision and Pattern Recognition 21
Replace Bogdan Sacaleanu with:
Bogdan Sacaleanu Germany
Benjamin Heinzerling Japan
Yuri Kuratov Russia
Sven Hertling Germany
Dennis Diefenbach France
Linyong Nan United States
C Gysel Netherlands
Emanuela Boroş France
Yew Ken Chia Singapore
Guillermo Carrascón Spain
Daniel Campos relative to Bogdan Sacaleanu Germany Bogdan Sacaleanu's profile →
Citations per field
00.5×8.7×
Bogdan Sacaleanu · 1×
Citations per year

Countries citing papers authored by Daniel Campos

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Campos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Daniel Campos, 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 Daniel Campos Line = papers co-authored together Daniel Campos links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 202046
2 202130
3 202129
4 202410
5 20217
6 20223
7 20223
8 20231
9 20231
10 20250
11 20250
12 20230

About Daniel Campos

Daniel Campos is a scholar working on Artificial Intelligence, Management Science and Operations Research, Information Systems, Sociology and Political Science and Geophysics, having authored 12 papers that have together received 130 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Data Quality and Management (5 papers), Natural Language Processing Techniques (4 papers), Advanced Text Analysis Techniques (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Information Retrieval and Search Behavior (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Artificial Intelligence (110 citations), Information Systems (58 citations), Health Informatics (3 citations), Management Science and Operations Research (14 citations) and Computer Vision and Pattern Recognition (21 citations). Daniel Campos has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Nick Craswell, Bhaskar Mitra, Emine Yılmaz, Jimmy Lin, Paul N. Bennett, Chenyan Xiong, Song Xia, Saurabh Tiwary, Ian Soboroff and Ellen M. Voorhees. Their work appears in journals such as Geologica Carpathica and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

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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