Intelligent Open Data Exploration
A classic unified, comprehensive platform that provides extensive access to open datasets through natural language queries in the fields of Cancer Biomarker Research, Research and Innovation Policy Making and Astrophysics; for a wide range of users from larger scientific communities to public.
Data growth and availability as well as data democratisation have radically changed data exploration in the last 10 years. INODE aims at simplifying access to data, by allowing a more dialectic and intuitive interaction with data using Natural Language Processing (NLP). The goal of INODE is to offer a suite of agile, fit-for-purpose and sustainable services for exploration of multiple open data sets; guidance for the users in understanding the data by providing insights into the data modal using visualisation techniques and offer suggestions of prospective datasets using recommender systems that can drive the user in formulating right NLP queries to find the right dataset efficiently; let user to explore the data extensively through in-built data analytic packages and to discover new insights/science through data visualisation.
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Authors: Sihem Amer-Yahia, Georgia Koutrika, Martin Braschler, Diego Calvanese, Davide Lanti, Hendrik Lücke-Tieke, Alessandro Mosca, Tarcisio Mendes de Farias, Dimitris Papadopoulos, Yogendra Patil, Guillem Rull, Ellery Smith, Dimitrios Skoutas, Srividya...
LILLIE: Information extraction and database integration using linguistics and learning-based algorithms
Authors: EllerySmith, Dimitris Papadopoulos, Martin Braschler, Kurt Stockinger Published in: Information Systems Abstract: Querying both structured and unstructured data via a single common query interface such as SQL or natural language has been a long standing...
INODE team members from CNRS and MPE recieved the best Demo paper award for the work "DORA THE EXPLORER: Exploring Very Large Data With Interactive Deep Reinforcement Learning" at CIKM 2021. 30th ACM International Conference on Information and Knowledge Management...
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 863410