Lisa Ehrlinger
Impact in
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- Data Quality and Management
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- Big Data and Business Intelligence
Papers in
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- Data Quality and Management 26
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- Data Mining Algorithms and Applications 9
- Software Engineering Research 3
- Co-authors
- Wolfram Wöß (17 shared papers)Verena Geist (4 shared papers)Lukas Fischer (2 shared papers)Rudolf Ramler (2 shared papers)Werner Zellinger (2 shared papers)David James Brunner (1 shared paper)Thomas Grubinger (1 shared paper)Thomas Natschläger (1 shared paper)
In The Last Decade
Lisa Ehrlinger
35 papers receiving 626 citations
Lisa Ehrlinger's Hit Papers
Peers
Comparison fields: 5 of 86
- Management Science and Operations Research 214
- Management Information Systems 115
- Artificial Intelligence 336
- Information Systems and Management 54
- Information Systems 159
Countries citing papers authored by Lisa Ehrlinger
This map shows the geographic impact of Lisa Ehrlinger'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 Lisa Ehrlinger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lisa Ehrlinger more than expected).
Fields of papers citing papers by Lisa Ehrlinger
This network shows the impact of papers produced by Lisa Ehrlinger. 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 Lisa Ehrlinger. The network helps show where Lisa Ehrlinger may publish in the future.
Co-authors
The 24 scholars most cited alongside Lisa Ehrlinger, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Towards a Definition of Knowledge Graphs. Hit paper breakdown → | 2016 | 309 |
| 2 | 2022 | 113 | |
| 3 | 2020 | 44 | |
| 4 | 2019 | 34 | |
| 5 | 2018 | 18 | |
| 6 | 2021 | 16 | |
| 7 | 2020 | 14 | |
| 8 | Automated Continuous Data Quality Measurement with QuaIIe | 2018 | 12 |
| 9 | 2019 | 10 | |
| 10 | Automated Data Quality Monitoring. | 2017 | 9 |
| 11 | 2022 | 7 | |
| 12 | 2025 | 5 | |
| 13 | 2019 | 5 | |
| 14 | 2021 | 4 | |
| 15 | 2021 | 4 | |
| 16 | 2021 | 4 | |
| 17 | 2023 | 4 | |
| 18 | QuaIIe: A Data Quality Assessment Tool for Integrated Information Systems | 2018 | 3 |
| 19 | Discovering DataOps: A Comprehensive Review of Definitions, Use Cases, and Tools | 2021 | 3 |
| 20 | 2023 | 3 |
About Lisa Ehrlinger
Lisa Ehrlinger is a scholar working on Management Science and Operations Research, Information Systems, Artificial Intelligence, Management Information Systems and Information Systems and Management, having authored 38 papers that have together received 651 indexed citations. Recurring topics across this work include Data Quality and Management (26 papers), Semantic Web and Ontologies (15 papers), Data Mining Algorithms and Applications (9 papers), Big Data and Business Intelligence (5 papers), Advanced Database Systems and Queries (5 papers), Business Process Modeling and Analysis (3 papers), Software Engineering Research (3 papers) and Advanced Graph Neural Networks (2 papers). The work is most often cited by research in Management Science and Operations Research (214 citations), Management Information Systems (115 citations), Artificial Intelligence (336 citations), Information Systems and Management (54 citations) and Information Systems (159 citations). Lisa Ehrlinger has collaborated with scholars based in Austria, Germany and Italy. Frequent co-authors include Wolfram Wöß, Verena Geist, Lukas Fischer, Rudolf Ramler, Werner Zellinger, David James Brunner, Thomas Grubinger, Thomas Natschläger, Mario Pichler and Georg Buchgeher. Their work appears in journals such as Journal of Data and Information Quality, IEEE Access, Frontiers in Big Data, ACM SIGMOD Record and Lecture notes in computer science.
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.