Joe Ellis
- Artificial Intelligence top 2%
- Management Science and Operations Research top 10%
- Information Systems top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition
- Co-authors
- Kira GriffittHeng JiHoa Trang DangRalph GrishmanStephanie StrasselZhiyi SongAnn BiesJonathan Wright
- Topics
- Natural Language Processing Techniques (9 papers)Topic Modeling (8 papers)Semantic Web and Ontologies (5 papers)
- Partner nations
- United StatesFranceSwitzerland
In The Last Decade
Joe Ellis
16 papers receiving 475 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 487
- Management Science and Operations Research 83
- Information Systems 73
- Molecular Biology 44
- Computer Vision and Pattern Recognition 24
Countries citing papers authored by Joe Ellis
This map shows the geographic impact of Joe Ellis'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 Joe Ellis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joe Ellis more than expected).
Fields of papers citing papers by Joe Ellis
This network shows the impact of papers produced by Joe Ellis. 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 Joe Ellis. The network helps show where Joe Ellis may publish in the future.
Co-authorship network of co-authors of Joe Ellis
This figure shows the co-authorship network connecting the top 25 collaborators of Joe Ellis. A scholar is included among the top collaborators of Joe Ellis 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 Joe Ellis. Joe Ellis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Laying the Groundwork for Knowledge Base Population: Nine Years of Linguistic Resources for TAC KBP. | 10 |
| 2 | 2 | |
| 3 | Overview of Linguistic Resources for the TAC KBP 2017 Evaluations: Methodologies and Results. | 19 |
| 4 | 7 | |
| 5 | 10 | |
| 6 | Overview of Linguistic Resources for the TAC KBP 2015 Evaluations: Methodologies and Results. | 31 |
| 7 | 91 | |
| 8 | 18 | |
| 9 | 48 | |
| 10 | Overview of Linguistic Resource for the TAC KBP 2014 Evaluations: Planning, Execution, and Results | 10 |
| 11 | 5 | |
| 12 | Linguistic Resources for Entity Linking Evaluation: from Monolingual to Cross-lingual | 4 |
| 13 | Linguistic Resources for 2012 Knowledge Base Population Evaluations | 13 |
| 14 | Annotation Trees: LDC's customizable, extensible, scalable, annotation infrastructure | 7 |
| 15 | Linguistic Resources for 2011 Knowledge Base Population Evaluation. | 3 |
| 16 | Overview of the TAC 2010 Knowledge Base Population Track | 251 |
About Joe Ellis
Joe Ellis is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine, having authored 16 papers that have together received 529 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (8 papers) and Semantic Web and Ontologies (5 papers). The work is most often cited by research in Artificial Intelligence (487 citations), Management Science and Operations Research (83 citations) and Information Systems (73 citations). Joe Ellis has collaborated with scholars based in United States, France and Switzerland. Frequent co-authors include Kira Griffitt, Heng Ji, Hoa Trang Dang, Ralph Grishman, Stephanie Strassel, Zhiyi Song, Ann Bies, Jonathan Wright, Justin L. Mott and Xiaoyi Ma. Their work appears in journals such as The American Journal of Cardiology, Language Resources and Evaluation and Coronary Artery Disease.
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.