INODE – Intelligent Open Data Exploration (EU Horizon 2020)
Description
Data growth and availability as well as data democratization have radically changed data exploration in the last 10 years. Many different data sets, generated by users, systems and sensors, are continuously being collected. These data sets contain information about scientific experiments, health, energy, education etc., and they are highly heterogeneous in nature, ranging from highly structured data in tabular form to unstructured text, images or videos. Furthermore, especially online content, is no longer the purview of large organizations. Open data repositories are made public and can benefit more types of users, from analysts exploring data sets for insight, scientists looking for patterns, to dashboard interactors and consumers looking for information. As a result, the benefit of data exploration becomes increasingly more prominent. However, the volume and complexity of data make it difficult for most users to access data in an easy way.In this project we propose INODE – Intelligent Open Data Exploration. The core principle of INODE is that users should interact with data in a more dialectic and intuitive way similar to a dialog with a human. To achieve this principle, INODE will offer a suite of agile, fit-for purpose and sustainable services for exploration of open data sets that help users (a) link and leverage multiple datasets, (b) access and search data using natural language, using examples and using analytics (c) get guidance from the system in understanding the data and formulating the right queries, and (d) explore data and discover new insights through visualizations.Our service offering is formed by and will initially respond to the needs of large and diverse scientific communities brought by our three use case providers: (a) Cancer Biomarker Research - SIB Swiss Institute of Bioinformatics, Switzerland, (b) Research and Innovation Policy Making - SIRIS, Spain, and (c) Astrophysics - Max Planck Institute for Extraterrestrial Physics, Germany.
Key Data
Projectlead
Project team
Prof. Dr. Martin Braschler, Ursin Brunner, Catherine Kosten, Dr. Farhad Nooralahzadeh, Ana-Claudia Sima, Ellery Smith, Yi Zhang
Project partners
ATHENA Research; Centre national de la recherche scientifique CNRS; Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.; Free University of Bozen-Bolzano; Infili Technologies P.C.; Max-Planck-Gesellschaft; SIRIS Academic SL; Swiss Institute of Bioinformatics SIB
Project status
completed, 11/2019 - 10/2022
Funding partner
Horizon 2020 / Projekt-Nr. 863410
Project budget
5'732'000 EUR
Further documents and links
Publications
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Evaluating the data model robustness of Text-to-SQL systems based on real user queries
2025 Fürst, Jonathan; Kosten, Catherine; Nooralahzadeh, Farhard; Zhang, Yi; Stockinger, Kurt
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ScienceBenchmark : a complex real-world benchmark for evaluating natural language to SQL systems
2024 Zhang, Yi; Deriu, Jan Milan; Katsogiannis-Meimarakis, George; Kosten, Catherine; Koutrika, Georgia; Stockinger, Kurt
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LILLIE : information extraction and database integration using linguistics and learning-based algorithms
2024 Smith, Ellery; Papadopoulos, Dimitris; Braschler, Martin; Stockinger, Kurt
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Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines
2024 Smith, Ellery; Paloots, Rahel; Giagkos, Dimitris; Baudis, Michael; Stockinger, Kurt
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Building natural language interfaces for databases in practice
2024 Lehmann, Claude; Gehrig, Dennis; Holdener, Stefan; Saladin, Carlo; Monteiro, João Pedro; Stockinger, Kurt
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Spider4SPARQL : a complex benchmark for evaluating knowledge graph question answering systems
2024 Kosten, Catherine; Cudré-Mauroux, Philippe; Stockinger, Kurt
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Improving NL-to-Query systems through re-ranking of semantic hypothesis
2022 von Däniken, Pius; Deriu, Jan Milan; Agirre, Eneko; Brunner, Ursin; Cieliebak, Mark; Stockinger, Kurt
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ValueNet : a natural language-to-SQL system that learns from database information
2021 Brunner, Ursin; Stockinger, Kurt
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INODE : building an end-to-end data exploration system in practice
2021 Amer-Yahia, Sihem; Koutrika, Georgia; Braschler, Martin; Calvanese, Diego; Lanti, Davide; Lücke-Tieke, Hendrik; Mosca, Alessandro; Mendes de Farias, Tarcisio; Papadopoulos, Dimitris; Patil, Yogendra; Rull, Guillem; Smith, Ellery; Skoutas, Dimitrios; Subramanian, Srividya; Stockinger, Kurt
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A methodology for creating question answering corpora using inverse data annotation
2020 Deriu, Jan Milan; Mlynchyk, Katsiaryna; Schläpfer, Philippe; Rodrigo, Alvaro; von Grünigen, Dirk; Kaiser, Nicolas; Stockinger, Kurt; Agirre, Eneko; Cieliebak, Mark