Tomasz Ksiezyk

739 citations
14 papers · 387 indexed · h-index 7
Topics
Semantic Web and Ontologies (8 papers)Advanced Database Systems and Queries (6 papers)Natural Language Processing Techniques (5 papers)
Partner nations
United States

In The Last Decade

Tomasz Ksiezyk

14 papers receiving 310 citations

Peers

Tomasz Ksiezyk
Comparison fields: 5 of 37
  • Artificial Intelligence 297
  • Computer Networks and Communications 229
  • Information Systems 173
  • Signal Processing 74
  • Management Information Systems 38
Replace M. Nodine with:
M. Nodine United States
J. Hammer United States
Andrzej Cichocki United States
S. H. Pakzad United States
Ayah Helal United Kingdom
Douglas K. Barry United States
V. De Antonellis Italy
Hans Albrecht Schmid Germany
T. A. Halpin Australia
C.J. Date United States
Tomasz Ksiezyk relative to M. Nodine United States M. Nodine's profile →
Citations per field
00.5×1.5×
M. Nodine · 1×
Citations per year

Countries citing papers authored by Tomasz Ksiezyk

Since Specialization
Citations

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

Fields of papers citing papers by Tomasz Ksiezyk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomasz Ksiezyk

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

All Works

14 of 14 papers shown
#WorkIndexed citations
1 3
2 11
3 31
4
Global information management via local autonomous agents
13
5 30
6 19
7 250
8 7
9
Carnot prototype
6
10
Equipment simulation for language understanding
5
11
Simulation-based understanding of texts about equipment
1
12
An equipment model and its role in the interpretation of noun phrases
3
13 4
14 4

About Tomasz Ksiezyk

Tomasz Ksiezyk is a scholar working on Artificial Intelligence, Computer Networks and Communications and Management Information Systems, having authored 14 papers that have together received 387 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (8 papers), Advanced Database Systems and Queries (6 papers) and Natural Language Processing Techniques (5 papers). The work is most often cited by research in Computer Networks and Communications (229 citations), Artificial Intelligence (297 citations) and Information Systems (173 citations). Tomasz Ksiezyk has collaborated with scholars based in United States. Frequent co-authors include J Fowler, Darrell Woelk, Ayah Helal, V. Kashyap, Md. Mamunur Rashid, M. Nodine, Roberto J. Bayardo, C. Unnikrishnan, A. Unruh and Guiomar Martín. Their work appears in journals such as ACM SIGMOD Record, Distributed and Parallel Databases and International Journal of Cooperative Information Systems.

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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