INODE – Intelligent Open Data Exploration (EU Horizon 2020)

At a glance

Project leader : Prof. Dr. Kurt Stockinger
Project team : Prof. Dr. Martin Braschler, Ursin Brunner, Catherine Kosten, Farhad Nooralahzadeh, Ana-Claudia Sima, Ellery Smith, Yi Zhang
Project budget : EUR 5'732'000
Project status : completed
Funding partner : EU and other international programmes (Horizon 2020 / Projekt-Nr. 863410)
Project partner : ATHENA Research, French National Centre for Scientific Research CNRS, Fraunhofer-Gesellschaft, Free University of Bozen-Bolzano, Infili Technologies P.C., Max-Planck-Gesellschaft, SIRIS Academic SL, Swiss Institute of Bioinformatics SIB
Contact person : Kurt Stockinger

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.

Further information

inode-project.eu

Publications

Smith, Ellery; Paloots, Rahel; Giagkos, Dimitris; Baudis, Michael; Stockinger, Kurt,

2024.

Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines.

Bioinformatics Advances.

Available from: https://doi.org/10.1093/bioadv/vbae045
Zhang, Yi; Deriu, Jan Milan; Katsogiannis-Meimarakis, George; Kosten, Catherine; Koutrika, Georgia; Stockinger, Kurt,

2024.

ScienceBenchmark : a complex real-world benchmark for evaluating natural language to SQL systems [paper].

In:

Proceedings of the VLDB Endowment.

50th International Conference on Very Large Data Bases, Guangzhou, China, 25-29 August 2024.

Association for Computing Machinery.

pp. 685-698.

Available from: https://doi.org/10.14778/3636218.3636225
Kosten, Catherine; Cudré-Mauroux, Philippe; Stockinger, Kurt,

2024.

Spider4SPARQL : a complex benchmark for evaluating knowledge graph question answering systems [paper].

In:

2023 IEEE International Conference on Big Data (BigData).

IEEE International Conference on Big Data, Sorrento, Italy, 15-18 December 2023.

IEEE.

Available from: https://doi.org/10.1109/BigData59044.2023.10386182
von Däniken, Pius; Deriu, Jan Milan; Agirre, Eneko; Brunner, Ursin; Cieliebak, Mark; Stockinger, Kurt,

2022.

Improving NL-to-Query systems through re-ranking of semantic hypothesis [paper].

In:

5th International Conference on Natural Language and Speech Processing (ICNLSP), online, 16-17 December 2022.

Winterthur:

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Available from: https://doi.org/10.21256/zhaw-26147
Lehmann, Claude; Gehrig, Dennis; Holdener, Stefan; Saladin, Carlo; Monteiro, João Pedro; Stockinger, Kurt,

2022.

Building natural language interfaces for databases in practice [paper].

In:

Proceedings of the 34th SSDBM.

34th International Conference on Scientific and Statistical Database Management (SSDBM), Copenhagen, Denmark, 6 - 8 July 2022.

Association for Computing Machinery.

Available from: https://doi.org/10.1145/3538712.3538744
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,

2021.

INODE : building an end-to-end data exploration system in practice.

SIGMOD Record.

50(4), pp. 23-29.

Available from: https://doi.org/10.21256/zhaw-23624
Brunner, Ursin; Stockinger, Kurt,

2021.

ValueNet : a natural language-to-SQL system that learns from database information [paper].

In:

Proceedings of the 37th ICDE.

37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021.

IEEE.

pp. 2177-2182.

Available from: https://doi.org/10.1109/ICDE51399.2021.00220
Smith, Ellery; Papadopoulos, Dimitris; Braschler, Martin; Stockinger, Kurt,

2021.

LILLIE : information extraction and database integration using linguistics and learning-based algorithms.

Information Systems.

105.

Available from: https://doi.org/10.1016/j.is.2021.101938
Deriu, Jan Milan; Mlynchyk, Katsiaryna; Schläpfer, Philippe; Rodrigo, Alvaro; von Grünigen, Dirk; Kaiser, Nicolas; Stockinger, Kurt; Agirre, Eneko; Cieliebak, Mark,

2020.

A methodology for creating question answering corpora using inverse data annotation [paper].

In:

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), online, 5-10 July 2020.

Association for Computational Linguistics.

pp. 897-911.

Available from: https://doi.org/10.18653/v1/2020.acl-main.84

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