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DOING 2021 : International Workshop on Intelligent Data – From Data to Knowledge


When Aug 24, 2021 - Aug 24, 2021
Where Tartu, Estonia
Submission Deadline Apr 9, 2021
Notification Due May 14, 2021

Call For Papers


Paper submission: April 9, 2021
Notification of acceptance: May 14, 2021


DOING workshop accepts 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: submitted papers 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.

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.

Papers must be submitted via EASY CHAIR (link TBD)


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.


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,
- 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
Urban economy and urban environments


Mírian Halfeld Ferrari Université d’Orléans, INSA CVL, LIFO EA, France
Carmem S. Hara Universidade Federal do Paraná, Curitiba, Brazil

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