Ahu Sieg
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
- Web Data Mining and Analysis
- Information Retrieval and Search Behavior
- Artificial Intelligence top 10%
- Semantic Web and Ontologies
- Topic Modeling
Papers in
-
- Recommender Systems and Techniques 6
- Information Retrieval and Search Behavior 5
- Web Data Mining and Analysis 4
- Expert finding and Q&A systems 2
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- Semantic Web and Ontologies 4
- Topic Modeling 1
- Co-authors
- Bamshad Mobasher (9 shared papers)Robin Burke (9 shared papers)Steven L. Lytinen (1 shared paper)
- Journals
- Journal of Religion and Health (1 paper)International Conference on Internet Computing (1 paper)
- Partner nations
- United States
In The Last Decade
Ahu Sieg
8 papers receiving 302 citations
Peers
Comparison fields: 5 of 33
- Information Systems 298
- Artificial Intelligence 188
- Signal Processing 60
- Computer Vision and Pattern Recognition 69
- Computer Science Applications 17
Countries citing papers authored by Ahu Sieg
This map shows the geographic impact of Ahu Sieg'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 Ahu Sieg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ahu Sieg more than expected).
Fields of papers citing papers by Ahu Sieg
This network shows the impact of papers produced by Ahu Sieg. 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 Ahu Sieg. The network helps show where Ahu Sieg may publish in the future.
Co-authors
The 3 scholars most cited alongside Ahu Sieg, 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 | 2007 | 176 | |
| 2 | Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search | 2007 | 55 |
| 3 | 2010 | 40 | |
| 4 | USING CONCEPT HIERARCHIES TO ENHANCE USER QUERIES IN WEB-BASED INFORMATION RETRIEVAL | 2003 | 29 |
| 5 | 2007 | 25 | |
| 6 | 2005 | 10 | |
| 7 | Concept Based Query Enhancement in the ARCH Search Agent. | 2003 | 9 |
| 8 | Ontology-Based Collaborative Recommendation. | 2010 | 9 |
| 9 | 2007 | 0 |
About Ahu Sieg
Ahu Sieg is a scholar working on Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing and Infectious Diseases, having authored 9 papers that have together received 353 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (6 papers), Information Retrieval and Search Behavior (5 papers), Semantic Web and Ontologies (4 papers), Web Data Mining and Analysis (4 papers), Advanced Image and Video Retrieval Techniques (2 papers), Expert finding and Q&A systems (2 papers), Data Management and Algorithms (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Information Systems (298 citations), Artificial Intelligence (188 citations), Signal Processing (60 citations), Computer Vision and Pattern Recognition (69 citations) and Computer Science Applications (17 citations). Ahu Sieg has collaborated with scholars based in United States. Frequent co-authors include Bamshad Mobasher, Robin Burke and Steven L. Lytinen. Their work appears in journals such as Journal of Religion and Health and International Conference on Internet Computing.
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.