Daniel Ruffinelli
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
- Bayesian Modeling and Causal Inference
- Semantic Web and Ontologies
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
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- Data Quality and Management
Papers in
-
- Advanced Graph Neural Networks 7
- Topic Modeling 6
- Bayesian Modeling and Causal Inference 4
- Quantum Computing Algorithms and Architecture 2
- Semantic Web and Ontologies 2
- Quantum Information and Cryptography 1
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- Data Quality and Management 3
- Co-authors
- Christian Meilicke (4 shared papers)Heiner Stuckenschmidt (4 shared papers)Rainer Gemulla (4 shared papers)Melisachew Wudage Chekol (2 shared papers)Samuel Broscheit (2 shared papers)Yanjie Wang (1 shared paper)Benjamı́n Barán (2 shared papers)Heiko Paulheim (1 shared paper)
- Journals
- Quantum Information Processing (1 paper)Lecture notes in computer science (3 papers)MADOC (University of Mannheim) (3 papers)MADOC (University of Mannheim) (1 paper)
- Partner nations
- GermanyParaguayUnited States
In The Last Decade
Daniel Ruffinelli
10 papers receiving 375 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 351
- Management Science and Operations Research 77
- Statistical and Nonlinear Physics 34
- Computational Theory and Mathematics 36
- Computer Vision and Pattern Recognition 39
Countries citing papers authored by Daniel Ruffinelli
This map shows the geographic impact of Daniel Ruffinelli'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 Ruffinelli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Ruffinelli more than expected).
Fields of papers citing papers by Daniel Ruffinelli
This network shows the impact of papers produced by Daniel Ruffinelli. 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 Ruffinelli. The network helps show where Daniel Ruffinelli may publish in the future.
Co-authors
The 13 scholars most cited alongside Daniel Ruffinelli, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 146 | |
| 2 | 2018 | 109 | |
| 3 | You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings | 2020 | 52 |
| 4 | 2020 | 49 | |
| 5 | 2017 | 13 | |
| 6 | 2017 | 6 | |
| 7 | 2022 | 3 | |
| 8 | 2019 | 3 | |
| 9 | 2016 | 2 | |
| 10 | Towards scalable ontological reasoning using machine learning | 2017 | 1 |
| 11 | 2024 | 0 |
About Daniel Ruffinelli
Daniel Ruffinelli is a scholar working on Artificial Intelligence, Management Science and Operations Research, Molecular Biology, Electrical and Electronic Engineering and Computer Networks and Communications, having authored 11 papers that have together received 384 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (7 papers), Topic Modeling (6 papers), Bayesian Modeling and Causal Inference (4 papers), Data Quality and Management (3 papers), Quantum Computing Algorithms and Architecture (2 papers), Low-power high-performance VLSI design (2 papers), Semantic Web and Ontologies (2 papers) and Quantum Information and Cryptography (1 paper). The work is most often cited by research in Artificial Intelligence (351 citations), Management Science and Operations Research (77 citations), Statistical and Nonlinear Physics (34 citations), Computational Theory and Mathematics (36 citations) and Computer Vision and Pattern Recognition (39 citations). Daniel Ruffinelli has collaborated with scholars based in Germany, Paraguay and United States. Frequent co-authors include Christian Meilicke, Heiner Stuckenschmidt, Rainer Gemulla, Melisachew Wudage Chekol, Samuel Broscheit, Yanjie Wang, Benjamı́n Barán, Heiko Paulheim, Kiril Gashteovski and Christopher Malon. Their work appears in journals such as Quantum Information Processing, Lecture notes in computer science, MADOC (University of Mannheim) and MADOC (University of Mannheim).
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.