Track 3

Track 3: Data Science, knowledge engineering, and ontologies (including Information Retrieval, Big Data, Databases and Knowledge Systems)

Track chairs:

  • Sana Sellami, Aix Marseille University LIS Laboratory, France
  • Mohamed Reda Bouadjenek, Deakin University, Australia

Scope

The Data science, Knowledge Engineering, and Ontologies Track welcomes submissions of original, high-quality research related to data analytics, the extraction of information, the analysis, recommendation, and mining of data content. We also encourage submissions that explore how people understand, engage and interact with data content through Information Retrieval, including discovery, recommendations or question answering, and Big Data, Databases and Knowledge Systems. Research on both theoretical and applied aspects of data science and Knowledge Engineering-related tasks is encouraged.

Topics
The topics of interest of the track include, but are not limited to:

- Data science

        -  Advanced data analytics

        -  Machine learning and data science ( Learning representations and features from data,  Machine learning algorithms for large-scale content mining)

        - Data cleaning

        - Data visualization

- Information Search and Retrieval

                - Query and document analysis, representation and understanding

                - Web search models, and ranking

                - Web recommender systems

                - Evaluation methodologies and metrics

- Knowledge engineering, and ontologies

                 - Ontologies and semantics  

                 - Techniques for the creation, curation, publication and consumption of   Knowledge Graphs, including Methods for Developing and Maintaining Shared Vocabularies/Ontologies

                  - Data Modeling and Inference

                  - Explanations and User-friendly Interaction

                   - Exploitation of Semantic Data for Machine Learning Tasks

Examples of application areas :
• Healthcare
• Social sciences
• Recommender platforms
• Logistics
• Transportation
• Urban planning
• Resource management (energy, water, air quality, waste management

PC Members:

  • Zafaryab Rasool, Deakin University
  • Ming Liu,  Deakin University
  • Ji Liu, Baidu Research
  • Frederic Flouvat, UNC University
  • Abderrahmane Maaradji, University Paris-Sorbone
  • Omar Al Zoubi, School of Computer Science
  • Hakim Hacid, Zayed University
  • Javier Espinosa, University of Lyon 3
  • Martin Musicante, Federal University of Rio Grande do Norte
  • Dou El Kefel Mansouri, University Ibn Khaldoun Tiaret
  • José Luis Zechinelli Martini, Universidad de las Amricas - Puebla
  • Mustapha Mebbah, University of Parıs 13