DOING : Intelligent Data from data to knowledge


August 24, 2021


A word about DOING

DOING workshop is connected to the working groups

  • DOING@DIAMS (part of the RTR DIAMS)
  • DOING@MADICS ( ACTION in the MADICS network)


GTM+3 (local time in Estonia)


  • 14:00 – Genoveva Vargas Solar, José Luis Zechinelli Martini, Javier Alfonso Espinosa-Oviedo and Luis M. Vilches Blásquez. LACLICHEV: Exploring the History of Climate Change in Latin America within Newspapers.
  • 14:30 – Dimmy Magalhaes , Ana Sodre, Luis Floriano, Aurora Pozo, Carmem Hara and Sidnei Machado. COVID-19 Portal: Machine learning techniques applied to the analysis of judicial processes related to the pandemic.
  • 15:00 – Ciro M. Medeiros , Martin Musicante and Umberto Costa. Standard Matching-Choice Expressions for defining Path Queries in Graph Databases.

15:30 -16:00 – BREAK


  • 16:00 – Ciro M. Medeiros and Martin Musicante and Mirian Halfeld-Ferrari. The Formal-Language-Constrained Graph Minimization Problem
  • 16:20 – Tatiane Araujo Muniz Lautert, Nadia Kozievitch, Ismael Villanueva Miranda and Monika Akbar. Public Health Units – Exploratory analysis for decision support.
  • 16:40 – Rufat Babayev and Lena Wiese. Interpreting decision-making process for multivariate time series classification

ACCEPTED PAPERS: In this second edition DOING received 9 papers: 3 of them were accepted as full papers and 3 as short papers. The acceptation rate is 50%.


  • Paper submission: April 9, 2021 April 23, 2021 (LAST EXTENDED DEADLINE): the site remains open until the beginning of the bidding phase on Saturday, April 24 at 5 p.m CEST (GMT +2)
  • Notification of acceptance: May 14, 2021 May 24, 2021
  • Camera-ready due: June 11, 2021 June 15, 2021
  • Workshop day: August 24, 2021

Aims and scope.

Text are important sources of information and communication in diverse domains. The intelligent, efficient and secure use of this information requires, in most cases, the transformation of unstructured textual data into data sets with some structure, and organized according to an appropriate schema that follows the semantics of an application domain. Indeed, solving the problems of modern society requires interdisciplinary research and information cross-referencing, thus surpassing the simple provision of unstructured data. There is a need for representations that are more flexible, subtle and context-sensitive, which can also be easily accessible via consultation tools and evolve according to these principles. In this context, consultation requires robust and efficient processing of requests, which may involve information analysis, with quality, consistency, and privacy preservation guarantees. Knowledge bases can be built as these new generation infrastructures which support data science queries on a user-friendly framework and are capable of providing the required machinery for advised decision-making.

The workshop focuses on transforming data into information and then into knowledge. The idea is to gather researchers in NLP (Natural Language Processing), DB (Databases), and AI (Artificial Intelligence) to discuss two main problems :

  • how to extract information from textual data and represent it in knowledge bases;
  • how to propose intelligent methods for handling and maintaining these databases with new forms of requests, including efficient, flexible, and secure analysis mechanisms, adapted to the user, and with quality and privacy preservation guarantees.

This workshop focuses on all aspects concerning these modern infrastructures, giving particular attention (but not limited to) to data related to health and environmental domains.

Topics of interest.

We invite the submission of work-in-progress that address various aspects of information extraction from textual data, intelligent and efficient interrogation, and maintenance of knowledge bases. The workshop welcomes submissions of theoretical, technical, experimental, methodological papers, application papers, position papers and papers on experience reports addressing – though not limited to – the following topics:

  • Artificial intelligence in databases and information systems
  • Data curation, annotation, and provenance
  • Data management and analytics
  • Data mining and knowledge discovery
  • Data models and query languages
  • Data quality and data cleansing
  • Data science (theory and techniques)
  • Context-aware and adaptive information systems
  • Constraints extraction from text
  • Natural language processing
  • Indexing, query processing and optimization
  • Information and knowledge extraction
  • Information integration
  • Information quality
  • Graph databases
  • Knowledge bases (querying, management, evolution and dynamics)
  • Machine learning for knowledge graph construction, completion, refinement
  • Machine learning for knowledge and information extraction, for instance, named entity disambiguation, sentiment analysis, relation extraction, or the detection of claims, facts and stances from unstructured documents
  • Machine Learning in NLP
  • Methodologies, models, algorithms, and architectures for applied data science
  • NLP for Digital Humanities
  • NLP & Knowledge Graphs
  • Privacy, trust and security in databases
  • Query processing and optimization
  • Question answering over knowledge graphs
  • Text databases

Preferred Application Domains (but not limited to).

  • Bio-sciences and healthcare
  • Environmental issues


DOING workshop intends to accept short (limited to 6 pages) and long (limited to 12 pages) papers. DOING reserves the right to accept as short papers those submitted as long, describing interesting and innovative ideas but still requiring further technical development. Papers should be written in English, formatted in Latex and present substantially original results. We adopt a double blind review policy: the papers submitted for review MUST NOT contain the authors’ names, affiliations, or any information that may disclose the authors’ identity. Authors should consult Springer’s authors’ guidelines and use their proceedings templates (you can download the templates available on the bottom of that page).

Papers must be submitted via EASY CHAIR : Track: Intelligent Data – From Data to Knowledge

Accepted papers will be published in the Springer CCIS series and the best papers will be invited to a special issue of the journal Computer Science and Information Systems.

Program Committee.

  • Cheikh Ba (UGB – Université Gaston Berger, Senegal)
  • Karin Becker – Universidade Federal do Rio Grande do Sul, Brazil
  • Javam de Castro Machado (UFC – Universidade Federal do Ceará, Brazil)
  • Laurent d’Orazio (IRISA, Université de Rennes, France)
  • Vasiliki Foufi (PhD, Switzerland)
  • Michel Gagnon (Polytechnique Montréal, Canada)
  • Sven Groppe (University of Lubeck, Germany)
  • Mingda Li (Pinterest, USA)
  • Jixue Liu (University of South Australia, Australia)
  • Anne-Lyse Minard-Forst (LLL, Université d’Orléans, France)
  • Damien Novel (ERTIM, INALCO, France)
  • Fathia Sais (LRI, Université Paris-Sud (Paris-Saclay), France)
  • Roberto Santana (University of the the Basque Country, Spain)
  • Agata Savary (LIFAT, Université de Tours, France)
  • Rebecca Schroeder Freitas (UDESC, Universidade Estadual de Santa Catarina, Brazil)
  • Aurora Trinidad Ramirez Pozo (UFPR – Universidade Federal do Paraná, Brazil)