Tahani Alsubait
-
- Online Learning and Analytics 4
- Artificial Intelligence top 10%
- Natural Language Processing Techniques 8
- Intelligent Tutoring Systems and Adaptive Learning 5
- Semantic Web and Ontologies 5
- Topic Modeling 3
- Machine Learning in Healthcare 2
- Sentiment Analysis and Opinion Mining 2
- Information Systems top 10%
- Educational Technology and Assessment 5
- Co-authors
- Bijan ParsiaUlrike SattlerHosam AlhakamiAbdullah BazMohammed A. Al GhamdiAlaa M. AlqahtaniAhmad M. AlghamdiRaees Ahmad Khan
- Partner nations
- Saudi ArabiaUnited KingdomPakistan
In The Last Decade
Tahani Alsubait
26 papers receiving 278 citations
Peers
Comparison fields: 5 of 74
- Computer Science Applications 67
- Artificial Intelligence 169
- Information Systems 88
- Health Informatics 4
- Health Information Management 13
Countries citing papers authored by Tahani Alsubait
This map shows the geographic impact of Tahani Alsubait'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 Tahani Alsubait with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tahani Alsubait more than expected).
Fields of papers citing papers by Tahani Alsubait
This network shows the impact of papers produced by Tahani Alsubait. 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 Tahani Alsubait. The network helps show where Tahani Alsubait may publish in the future.
Co-authorship network
The 16 scholars most cited alongside Tahani Alsubait, 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 | 2026 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 3 | |
| 6 | 2023 | 0 | |
| 7 | 2021 | 18 | |
| 8 | 2021 | 20 | |
| 9 | 2021 | 21 | |
| 10 | 2020 | 5 | |
| 11 | 2020 | 5 | |
| 12 | 2020 | 20 | |
| 13 | 2020 | 5 | |
| 14 | ONTOLOGY-BASED MULTIPLE-CHOICE QUESTION GENERATION | 2015 | 3 |
| 15 | 2015 | 35 | |
| 16 | Measuring Conceptual Similarity in Ontologies: How Bad is a Cheap Measure? | 2014 | 1 |
| 17 | Generating multiple choice questions from ontologies: Lessons learnt | 2014 | 26 |
| 18 | Mining Ontologies for Analogy Questions: A Similarity-based Approach. | 2012 | 13 |
| 19 | 2012 | 12 | |
| 20 | 2012 | 7 |
About Tahani Alsubait
Tahani Alsubait is a scholar working on Computer Science Applications, Artificial Intelligence and Health Information Management, having authored 31 papers that have together received 296 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (8 papers), Intelligent Tutoring Systems and Adaptive Learning (5 papers), Educational Technology and Assessment (5 papers), Semantic Web and Ontologies (5 papers), Online Learning and Analytics (4 papers), Topic Modeling (3 papers), Machine Learning in Healthcare (2 papers) and Sentiment Analysis and Opinion Mining (2 papers). The work is most often cited by research in Computer Science Applications (67 citations), Artificial Intelligence (169 citations) and Information Systems (88 citations). Tahani Alsubait has collaborated with scholars based in Saudi Arabia, United Kingdom and Pakistan. Frequent co-authors include Bijan Parsia, Ulrike Sattler, Hosam Alhakami, Abdullah Baz, Mohammed A. Al Ghamdi, Alaa M. Alqahtani, Ahmad M. Alghamdi, Raees Ahmad Khan, Badr Alsolami and Mazin Alshamrani. Their work appears in journals such as IEEE Access, Applied Sciences, Scientific Reports, Arabian Journal for Science and Engineering and Artificial Intelligence Review.
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.