DARE Lab | George Katsogiannis

I am working as a research assistant at the Athena Research and Innovation Center, under the supervision of Dr. Georgia Koutrika. My work focuses on using Deep Learning and Natural Language Processing techniques to make data open and accessible to users without technical knowledge, and is part of the INODE and FAIR Core 4 EOSC projects, funded by the EU. During the past two years my work has resulted in 5 tutorials presented at major conferences [EDBT'21, SIGMOD'21, TWC'22, WSDM'23, VLDB'23], one query explanation system [eQsplain] that has been integrated in the INODE project, and a survey studying the fast-paced research area of deep learning text-to-SQL [VLDBJ'23].

My MSc degree was in Data Science and Information Technologies, with a specialization in Artificial Intelligence, at the University of Athens. My MSc thesis is titled "Data Democratisation with Deep Learning: Structured Query Translation from and to Natural Language". Prior to that, I was studying towards my BSc degree in Computer Science at the University of Athens, where I completed my BSc thesis, titled "Translating Natural Language to SQL using Deep Learning".

Selected Work

eQsplain: Generate query explanations with the power of Transformers
A Survey and Taxonomy of Deep Learning Text-to-SQL Systems: Understanding how far we have come and what lies ahead in the fast-paced area of Text-to-SQL with Deep Learning

INODE: Working on user assistant services with a focus on query explanations
FAIR Core 4 EOSC: Team leader for DARE Lab, also working on NL search services

Publications

2024

VLDB

ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems

Zhang, Yi, Deriu, Jan, Katsogiannis-Meimarakis, George, Kosten, Catherine, Koutrika, Georgia, and Stockinger, Kurt

Proceedings of the VLDB Endowment

2023

VLDB

Natural Language Interfaces for Databases with Deep Learning

Katsogiannis-Meimarakis, George, Xydas, Mike, and Koutrika, Georgia

Proc. VLDB Endow. Sep

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In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.
BigVis

Natural Language Data Interfaces: From Keyword Search to ChatGPT, Are We There Yet?

Katsogiannis-Meimarakis, George, Tsapelas, Christos, and Koutrika, Georgia

In Proceedings of the 6th International Workshop on Big Data Visual Exploration and Analytics co-located with EDBT/ICDT 2023 Joint Conference

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WSDM

Data Democratisation with Deep Learning: The Anatomy of a Natural Language Data Interface

Katsogiannis-Meimarakis, George, Xydas, Mike, and Koutrika, Georgia

In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

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In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.
VLDBJ
A Survey on Deep Learning Approaches for Text-to-SQL

Katsogiannis-Meimarakis, George, and Koutrika, Georgia

The VLDB Journal

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To bridge the gap between users and data, numerous text-to-SQL systems have been developed that allow users to pose natural language questions over relational databases. Recently, novel text-to-SQL systems are adopting deep learning methods with very promising results. At the same time, several challenges remain open making this area an active and flourishing field of research and development. To make real progress in building text-to-SQL systems, we need to de-mystify what has been done, understand how and when each approach can be used, and, finally, identify the research challenges ahead of us. The purpose of this survey is to present a detailed taxonomy of neural text-to-SQL systems that will enable a deeper study of all the parts of such a system. This taxonomy will allow us to make a better comparison between different approaches, as well as highlight specific challenges in each step of the process, thus enabling researchers to better strategise their quest towards the “holy grail” of database accessibility.
@article{katsogiannis2023survey, author = {Katsogiannis-Meimarakis, George and Koutrika, Georgia}, title = {A Survey on Deep Learning Approaches for Text-to-SQL}, journal = {The VLDB Journal}, year = {2023}, issn = {0949-877X}, doi = {10.1007/s00778-022-00776-8}, url = {https://doi.org/10.1007/s00778-022-00776-8}, html = {https://doi.org/10.1007/s00778-022-00776-8}, abbr = {VLDBJ}, bibtex_show = {true}, selected = {true} }

2022

TWC

Data Democratisation with Deep Learning: An Analysis of Text-to-SQL Systems

Katsogiannis-Meimarakis, George, and Koutrika, Georgia

In Companion Proceedings of the Web Conference 2022

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In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and increasing complexity of data makes data querying all the more challenging even for experts. For this reason, numerous text-to-SQL systems have been developed that enable querying relational databases using natural language. The recent advances on deep neural networks along with the creation of two large datasets specifically made for training text-to-SQL systems, have paved the path for a novel and very promising research area. The purpose of this tutorial is a deep dive into this area, covering state-of-the-art techniques for natural language representation in neural networks, benchmarks that sparked research and competition, recent text-to-SQL systems using deep learning techniques, as well as open problems and research opportunities.

2021

SIGMOD
A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems

Katsogiannis-Meimarakis, George, and Koutrika, Georgia

In Proceedings of the 2021 International Conference on Management of Data

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Data is a prevalent part of every business and scientific domain,but its explosive volume and increasing complexity make data querying challenging even for experts. For this reason, numerous text-to-SQL systems have been developed that enable querying relational databases using natural language. The recent advances on deep neural networks along with the creation of two large datasets specifically made for training text-to-SQL systems, have paved the path for a novel and very promising research area. The purpose of this tutorial is a deep dive into this area, covering state-of-the-art techniques for natural language representation in neural networks,benchmarks that sparked research and competition, recent text-to-SQL systems using deep learning techniques, as well as open problems and research opportunities.
@inproceedings{10.1145/3448016.3457543, author = {Katsogiannis-Meimarakis, George and Koutrika, Georgia}, title = {A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems}, year = {2021}, isbn = {9781450383431}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3448016.3457543}, doi = {10.1145/3448016.3457543}, booktitle = {Proceedings of the 2021 International Conference on Management of Data}, pages = {2846–2851}, numpages = {6}, keywords = {deep learning, text-to-SQL}, location = {Virtual Event, China}, series = {SIGMOD/PODS '21}, html = {https://doi.org/10.1145/3448016.3457543}, bibtex_show = {true}, abbr = {SIGMOD} }
EDBT
Deep Learning Approaches for Text-to-SQL Systems.

Katsogiannis-Meimarakis, George, and Koutrika, Georgia

In EDBT

Bib
@inproceedings{katsogiannis2021deep, title = {Deep Learning Approaches for Text-to-SQL Systems.}, author = {Katsogiannis-Meimarakis, George and Koutrika, Georgia}, booktitle = {EDBT}, pages = {710--713}, year = {2021}, bibtex_show = {true}, abbr = {EDBT} }