Daniel Ruffinelli
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Semantic Web and Ontologies
- Topic Modeling
- Quantum Computing Algorithms and Architecture
- Explainable Artificial Intelligence (XAI)
Papers in
-
- Advanced Graph Neural Networks 8
- Semantic Web and Ontologies 4
- Quantum Computing Algorithms and Architecture 2
- Topic Modeling 1
- Co-authors
- Christian Meilicke (5 shared papers)Heiner Stuckenschmidt (4 shared papers)Rainer Gemulla (5 shared papers)Melisachew Wudage Chekol (2 shared papers)Samuel Broscheit (3 shared papers)Yanjie Wang (2 shared papers)Benjamı́n Barán (2 shared papers)Heiko Paulheim (1 shared paper)
- Journals
- Quantum Information Processing (1 paper)Lecture notes in computer science (3 papers)International Joint Conference on Artificial Intelligence (1 paper)Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) (2 papers)MADOC (University of Mannheim) (2 papers)
- Partner nations
- GermanyUnited StatesParaguay
In The Last Decade
Daniel Ruffinelli
11 papers receiving 450 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 416
- Space and Planetary Science 1
- Computational Theory and Mathematics 15
- Information Systems 19
- Management Information Systems 7
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 12 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 | 154 | |
| 2 | 2018 | 124 | |
| 3 | You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings | 2020 | 67 |
| 4 | 2020 | 54 | |
| 5 | 2019 | 29 | |
| 6 | 2017 | 13 | |
| 7 | 2017 | 6 | |
| 8 | 2019 | 4 | |
| 9 | 2016 | 3 | |
| 10 | 2022 | 3 | |
| 11 | Towards scalable ontological reasoning using machine learning | 2017 | 1 |
| 12 | 2024 | 0 |
About Daniel Ruffinelli
Daniel Ruffinelli is a scholar working on Artificial Intelligence, Nuclear Energy and Engineering, Waste Management and Disposal, Infectious Diseases and Organic Chemistry, having authored 12 papers that have together received 458 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Semantic Web and Ontologies (4 papers), Quantum Computing Algorithms and Architecture (2 papers) and Topic Modeling (1 paper). The work is most often cited by research in Artificial Intelligence (416 citations), Space and Planetary Science (1 citation), Computational Theory and Mathematics (15 citations), Information Systems (19 citations) and Management Information Systems (7 citations). Daniel Ruffinelli has collaborated with scholars based in Germany, United States and Paraguay. Frequent co-authors include Christian Meilicke, Heiner Stuckenschmidt, Rainer Gemulla, Melisachew Wudage Chekol, Samuel Broscheit, Yanjie Wang, Benjamı́n Barán, Heiko Paulheim, Graham Neubig and Kiril Gashteovski. Their work appears in journals such as Quantum Information Processing, Lecture notes in computer science, International Joint Conference on Artificial Intelligence, Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) 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.