View on GitHub

The Web Conference 2022 Tutorial: Assessing Research Impact by Leveraging Open Scholarly Knowledge Graphs


Nowadays, the vast amount of existing published research works creates major obstacles regarding the traditional knowledge discovery required by common research processes and other relevant tasks. However, at the same time, the increased popularity of the Open Science movement, makes large amounts of scholarly metadata available through open Scholarly Knowledge Graphs (SKGs), paving the way for reliable research impact assessment processes, that can alleviate the aforementioned issue. The main objective of the tutorial is to inform and educate the audience about the opportunities and challenges that open SKGs create in the field of research impact assessment, presenting the respective state-of-the-art and highlighting common pitfalls.

Impact Assessment in SKGs

The availability of scholarly metadata cannot by itself guarantee the reliability of research impact procedures. Since the landscape of the available measures for impact assessment is extremely crowded, with many of them sharing similar key concepts and ideas, it is really difficult for researchers, developers, and other interested stakeholders to identify which are the strengths and weaknesses of each approach and which measures are more suitable for the impact assessment services they target to offer. This is extremely important since it is now widely accepted that research impact is a complex notion, having many, and not always correlated, aspects, and that different measures capture impact from diverse perspectives, each of which could be more important in different applications. Hence the selection of the appropriate research impact measures should be done with caution.

The main objective of the tutorial is to inform and educate the audience about the opportunities and challenges that open Scholarly Knowledge Graphs (SKGs) create in research impact assessment, presenting the respective state-of-the-art impact measures that can be calculated, analysing their strengths, weaknesses, and proper uses, and highlighting the most common pitfalls. Although the tutorial will discuss impact from various perspectives, the focus will be given on the different aspects of citation-based, scientific impact. The tutorial will be concluded with the demonstration of the use of different impact measures in practical real-world applications and the discussion of the current open challenges and the well-anticipated needs of the research community at large regarding reliable processes for impact assessment.

Tutorial Material

  1. Part A: Introduction
    • History
    • Motivation and applications
    • Open SKGs
    • Multi-perspective impact assessment
  2. Part B: Research impact assessment approaches
    • Background
    • Classification of approaches
    • Notable approaches
    • Strengths and weaknesses
  3. Part C: Aftermath
    • Demonstrating applications
    • Open research challenges
    • Conclusions

Video Teaser

Impact Assessment Tutorial Teaser


Thanasis Vergoulis

hiThanasis Vergoulis is a Scientific Associate at IMSI, Athena Research Center in Greece. He received his diploma in Computer Engineering and Informatics from the Univ. of Patras and his PhD in Computer Science from NTU of Athens, under the supervision of Prof. Timos Sellis. He has been involved in several EU and national ICT projects related to big data, scientific data management, open science, and linked data. His research interests also involve bioinformatics, text mining & information retrieval for scientific publications, and research analytics. He has served as a member of the program or organizing committee for several CS conferences and as a reviewer for several top-tier journals. Finally, he has been teaching courses in undergraduate and postgraduate level in academic institutions in Greece and Cyprus.

Dimitris Sacharidis

hi Dimitris Sacharidis is an assistant professor at the Web and Information Technologies lab at the Université Libre De Bruxelles (ULB). Prior to that he was an assistant professor at the Technical University of Vienna, and a Marie Skłodowska Curie fellow at the “Athena” Research Center and at the Hong Kong University of Science and Technology. He has a Diploma and a PhD in Computer Engineering from the National Technical University of Athens, and a MSc in Computer Science from the University of Southern California. In his research, he is interested in topics related to data science and data engineering.

Ilias Kanellos

hi Ilias Kanellos is a Scientific Associate at IMSI - "Athena" RC. He received his diploma in Electrical and Computer Engineering from the NTU of Athens in 2012. He then completed his PhD at the school of Electrical and Computer Engineering, NTU Athens, in 2020. He has been involved in several EU and national R&D projects and his research interests include research analytics, scientific data management, cloud computing, bioinformatics, and data mining. He has co-authored 18 articles in Scientific Journals and Conferences.