Joe Tekli
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Networks and Communications top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Co-authors
- Richard ChbeirKokou YétongnonAgma J. M. TrainaCaetano TrainaErnesto DamianiWilliam I. GroskyGabriele GianiniCarlos Raymundo
- Topics
- Semantic Web and Ontologies (20 papers)Advanced Database Systems and Queries (17 papers)Data Management and Algorithms (9 papers)
In The Last Decade
Joe Tekli
49 papers receiving 724 citations
Peers
Comparison fields: 5 of 93
- Artificial Intelligence 364
- Information Systems 202
- Computer Networks and Communications 175
- Computer Vision and Pattern Recognition 171
- Signal Processing 119
Countries citing papers authored by Joe Tekli
This map shows the geographic impact of Joe Tekli'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 Tekli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joe Tekli more than expected).
Fields of papers citing papers by Joe Tekli
This network shows the impact of papers produced by Joe Tekli. 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 Tekli. The network helps show where Joe Tekli may publish in the future.
Co-authorship network of co-authors of Joe Tekli
This figure shows the co-authorship network connecting the top 25 collaborators of Joe Tekli. A scholar is included among the top collaborators of Joe Tekli 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 Tekli. Joe Tekli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 62 | |
| 5 | 3 | |
| 6 | 34 | |
| 7 | 22 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | 4 | |
| 11 | 5 | |
| 12 | 6 | |
| 13 | 2 | |
| 14 | 6 | |
| 15 | 26 | |
| 16 | 13 | |
| 17 | Towards efficient horizontal multimedia database fragmentation using semantic-based predicates implication | 8 |
| 18 | Efficient XML Structural Similarity Detection using Sub-tree Commonalities | 11 |
| 19 | 4 | |
| 20 | Semantic and Structure Based XML Similarity: An Integrated Approach. | 8 |
About Joe Tekli
Joe Tekli is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 51 papers that have together received 750 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (20 papers), Advanced Database Systems and Queries (17 papers) and Data Management and Algorithms (9 papers). The work is most often cited by research in Artificial Intelligence (364 citations), Signal Processing (119 citations) and Information Systems (202 citations). Joe Tekli has collaborated with scholars based in Lebanon, France and Brazil. Frequent co-authors include Richard Chbeir, Kokou Yétongnon, Agma J. M. Traina, Caetano Traina, Ernesto Damiani, William I. Grosky, Gabriele Gianini, Carlos Raymundo, Alceu Ferraz Costa and Marco Viviani. Their work appears in journals such as Communications of the ACM, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.
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.