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Artificial Intelligence
 
Data Science
 
Algorithms

Enhancing Entity Resolution and Retrieval through Dataset Decomposition - Yannis Velegrakis (Utrecht University)

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Dates
2025-07-09 16:00 - 17:00

Title: Enhancing Entity Resolution and Retrieval through Dataset Decomposition

Speaker: Prof. Yannis Velegrakis (Utrecht University, The Netherlands)

Abstract: Any traditional data management process is typically applied to some dataset. We advocate that slicing the dataset and treating each slice differently can lead to better performance in a number of different scenarios. We present two works where this principle has been applied and we demonstrate its effectiveness. First, we show that even for short documents (forum posts), slicing allows for better discovery of documents related to a post at hand. Second, we illustrate how entity resolution can benefit from slicing. It is known that different entity resolution algorithms perform better on different datasets. We bring this idea within a dataset. We first slice the dataset and then select for each slice the method that performs best for each slice. Doing so leads to an improved overall performance of the entity resolution process. The two main challenges in this approach are to decide how to do the slicing, and how to select the resolution method that is best for a slice.

Short Biography: Yannis Velegrakis  is a Computer Science professor at Utrecht University (Netherlands) where he holds the chair on Very Large Data Management, heads the Data Intensive Systems Group, and leads the Master’s programme in Data Science. His research area of expertise includes Data Preparation and Curation, Data Quality, Big Data Management, Knowledge Engineering, Graph Management, and Highly Heterogeneous Information Integration. He holds a PhD degree in Computer Science from the University of Toronto. He has been a professor at the University of Trento and a researcher at the AT&T Research Labs. He is also a PI at the Archimedes Unit of the Athena Research Center. He has spent time for research work at IBM Almaden Research Center, the Huawei European Research Center in Munich, the Center of Advanced Studies of the IBM Toronto Lab, the University of California, Santa-Cruz, and the University of Paris-Saclay. He has been the general chair of VLDB 2013 and ICDE 2024, the PC Chair of EDBT 2021, and area chair in multiple VLDB, SIGMOD, ICDE, and EDBT Conferences. He is currently serving on the board of the EDBT Association (as president), on the VLDB Board of Trustees, on the SIKS Research School board, and as associate editor for Systems on the SIGMOD Record editorial team.

 
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Vision

To position Greece as a leading player in AI and Data Science

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Mission

To build an AI Excellence Hub in Greece where the international research community can connect, groundbreaking ideas can thrive, and the next generation of scientists emerges, shaping a brighter future for Greece and the world

 

Welcome to ARCHIMEDES, a vibrant research hub connecting the global AI and Data Science research community fostering groundbreaking research in Greece and beyond. Its dedicated core team, comprising lead researchers, affiliated researchers, Post-Docs, PhDs and interns, is committed to advancing basic and applied research in Artificial Intelligence and its supporting disciplines, including Algorithms, Statistics, Learning Theory, and Game Theory organized around 8 core research areas. By collaborating with Greek and Foreign Universities and Research Institutes, ARCHIMEDES disseminates its research findings fostering knowledge exchange and providing enriching opportunities for students. Leveraging AI to address real-world challenges, ARCHIMEDES promotes innovation within the Greek ecosystem and extends its societal impact. Established in January 2022, as a research unit of the Athena Research Center with support from the Committee Greece 2021, ARCHIMEDES is funded for its first four years by the EU Recovery and Resilience Facility (RRF).

 
 

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Today, we celebrate the historical, cultural, and linguistic significance of the Greek language. While Standard Modern Greek often takes center stage, we at Archimedes - AI and Data Science Research Hub recognize the impressive diversity and great cultural significance of its numerous dialects. These dialects present both exciting opportunities and complex challenges for AI and Large Language Models (LLMs) because each one of them presents unique linguistic features and all of them are low resourced. That’s why we’re using cutting-edge AI to document, digitize, and analyze these invaluable linguistic treasures, ensuring their preservation and accessibility for generations to come.

 
 

The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

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