Mehdi Allahyari

853 total citations
12 papers, 135 citations indexed

About

Mehdi Allahyari is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Mehdi Allahyari has authored 12 papers receiving a total of 135 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 8 papers in Information Systems and 2 papers in Molecular Biology. Recurrent topics in Mehdi Allahyari's work include Topic Modeling (7 papers), Advanced Text Analysis Techniques (6 papers) and Web Data Mining and Analysis (6 papers). Mehdi Allahyari is often cited by papers focused on Topic Modeling (7 papers), Advanced Text Analysis Techniques (6 papers) and Web Data Mining and Analysis (6 papers). Mehdi Allahyari collaborates with scholars based in United States, China and Italy. Mehdi Allahyari's co-authors include Krys J. Kochut, Seyedamin Pouriyeh, Maciej Janik, Hamid R. Arabnia, Gong Cheng, Maurizio Atzori, Krzysztof Kochut, Farid Ghareh Mohammadi, Mary R. Galinski and Elizabeth D. Trippe and has published in prestigious journals such as International Journal of Advanced Computer Science and Applications, International Journal of Semantic Computing and UNICA IRIS Institutional Research Information System (University of Cagliari).

In The Last Decade

Mehdi Allahyari

11 papers receiving 128 citations

Peers

Mehdi Allahyari

AI

IS

MSOR

MB

CNC

Makbule Gülçin Özsoy Türkiye

Marco Cornolti Italy

Francesco Corcoglioniti Italy

Trapit Bansal United States

Elias Zavitsanos Greece

Paramita Mirza Germany

Leonhard Hennig Germany

Eraldo Rezende Fernandes Brazil

Rianne Kaptein Netherlands

Erdal Kuzey Germany

Makbule Gülçin Özsoy Türkiye

Mehdi Allahyari

174 ×1.7

AI

73 ×1.6

IS

21 ×0.8

MSOR

9 ×0.6

MB

15 ×1.2

CNC

Citations per year, relative to Mehdi Allahyari Mehdi Allahyari (= 1×) peers Makbule Gülçin Özsoy

Countries citing papers authored by Mehdi Allahyari

Since Specialization

Citations

This map shows the geographic impact of Mehdi Allahyari'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 Mehdi Allahyari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mehdi Allahyari more than expected).

Fields of papers citing papers by Mehdi Allahyari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mehdi Allahyari. 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 Mehdi Allahyari. The network helps show where Mehdi Allahyari may publish in the future.

Co-authorship network of co-authors of Mehdi Allahyari

This figure shows the co-authorship network connecting the top 25 collaborators of Mehdi Allahyari. A scholar is included among the top collaborators of Mehdi Allahyari 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 Mehdi Allahyari. Mehdi Allahyari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Allahyari, Mehdi, Seyedamin Pouriyeh, & Krys J. Kochut. (2019). Combining semantic graph and probabilistic topic models for discovering coherent topics. 17(4). 365–379. 2 indexed citations

2.

Pouriyeh, Seyedamin, Mehdi Allahyari, Gong Cheng, et al.. (2019). Ontology Summarization: Graph-Based Methods and Beyond. International Journal of Semantic Computing. 13(2). 259–283. 12 indexed citations

3.

Pouriyeh, Seyedamin, Mehdi Allahyari, Krys J. Kochut, Gong Cheng, & Hamid R. Arabnia. (2018). Combining Word Embedding and Knowledge-Based Topic Modeling for Entity Summarization. 252–255. 9 indexed citations

4.

Pouriyeh, Seyedamin, Mehdi Allahyari, Gong Cheng, et al.. (2018). Graph-Based Methods for Ontology Summarization: A Survey. UNICA IRIS Institutional Research Information System (University of Cagliari). 85–92. 10 indexed citations

5.

Pouriyeh, Seyedamin, Mehdi Allahyari, Krzysztof Kochut, Gong Cheng, & Hamid R. Arabnia. (2017). ES-LDA: Entity Summarization using Knowledge-based Topic Modeling. International Joint Conference on Natural Language Processing. 1. 316–325. 17 indexed citations

6.

Allahyari, Mehdi, et al.. (2017). A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling. International Journal of Advanced Computer Science and Applications. 8(9). 13 indexed citations

7.

Trippe, Elizabeth D., Mustafa V. Nural, Mehdi Assefi, et al.. (2017). A Vision for Health Informatics: Introducing the SKED Framework. 1 indexed citations

8.

Allahyari, Mehdi & Krys J. Kochut. (2016). Discovering Coherent Topics with Entity Topic Models. 26–33. 10 indexed citations

9.

Allahyari, Mehdi & Krys J. Kochut. (2016). Semantic Tagging Using Topic Models Exploiting Wikipedia Category Network. 63–70. 8 indexed citations

10.

Allahyari, Mehdi & Krys J. Kochut. (2016). Using Semantically-Extended LDA Topic Model for Semantic Tagging. International Journal of Semantic Computing. 10(4). 503–525.

11.

Allahyari, Mehdi & Krys J. Kochut. (2015). Automatic Topic Labeling Using Ontology-Based Topic Models. 259–264. 24 indexed citations

12.

Allahyari, Mehdi, Krys J. Kochut, & Maciej Janik. (2014). Ontology-Based Text Classification into Dynamically Defined Topics. 273–278. 29 indexed citations

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.

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