David Ferrucci
- Artificial Intelligence top 0.5%
- Topic Modeling 15
- Natural Language Processing Techniques 13
- Semantic Web and Ontologies 11
- Bayesian Modeling and Causal Inference 2
- Logic, Reasoning, and Knowledge 2
- Health Informatics top 2%
- Information Systems top 1%
- Software Engineering Research 5
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- Advanced Database Systems and Queries 2
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- Data Quality and Management 2
David Ferrucci
27 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 144
- Artificial Intelligence 1.8k
- Health Informatics 70
- Information Systems 529
- Information Systems and Management 134
- Health Information Management 77
Countries citing papers authored by David Ferrucci
This map shows the geographic impact of David Ferrucci'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 David Ferrucci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Ferrucci more than expected).
Fields of papers citing papers by David Ferrucci
This network shows the impact of papers produced by David Ferrucci. 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 David Ferrucci. The network helps show where David Ferrucci may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David Ferrucci, 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 | Braid: Weaving Symbolic and Statistical Knowledge into Coherent Logical Explanations. | 2020 | 0 |
| 2 | 2017 | 36 | |
| 3 | 2012 | 204 | |
| 4 | 2012 | 33 | |
| 5 | Introduction to “This is Watson”breakdown → | 2012 | 272 |
| 6 | 2012 | 91 | |
| 7 | 2011 | 19 | |
| 8 | PRISMATIC: Inducing Knowledge from a Large Scale Lexicalized Relation Resource | 2010 | 17 |
| 9 | A Multi-Strategy and Multi-Source Approach to Question Answering | 2006 | 23 |
| 10 | 2005 | 0 | |
| 11 | Text Analysis as Formal Inference for the Purposes of Uniform Tracing and Explanation Generation | 2004 | 1 |
| 12 | Software Architectures for Advanced QA. | 2004 | 1 |
| 13 | UIMA: an architectural approach to unstructured information processing in the corporate research environmentbreakdown → | 2004 | 546 |
| 14 | 2004 | 91 | |
| 15 | 2003 | 9 | |
| 16 | Hybridization in Question Answering Systems | 2003 | 9 |
| 17 | 2001 | 53 | |
| 18 | 1999 | 35 | |
| 19 | 1999 | 3 | |
| 20 | 1998 | 14 |
About David Ferrucci
David Ferrucci is a scholar working on Artificial Intelligence, Information Systems, Information Systems and Management, Health Information Management and Management Science and Operations Research, having authored 31 papers that have together received 2.5k indexed citations. Recurring topics across this work include Topic Modeling (15 papers), Natural Language Processing Techniques (13 papers), Semantic Web and Ontologies (11 papers), Software Engineering Research (5 papers), Bayesian Modeling and Causal Inference (2 papers), Logic, Reasoning, and Knowledge (2 papers), Advanced Database Systems and Queries (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Artificial Intelligence (1.8k citations), Health Informatics (70 citations), Information Systems (529 citations), Information Systems and Management (134 citations) and Health Information Management (77 citations). David Ferrucci has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Adam Lally, David Gondek, Aditya Kalyanpur, Selmer Bringsjord, Jennifer Chu‐Carroll, John Prager, James Fan, Jaimie Murdock, Chris Welty and Eric Nyberg. Their work appears in journals such as IBM Journal of Research and Development, Minds and Machines, AI Magazine, Natural Language Engineering and Artificial Intelligence.
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.