{"id":398,"date":"2025-02-13T15:20:16","date_gmt":"2025-02-13T15:20:16","guid":{"rendered":"https:\/\/conferences.sigappfr.org\/aiccsa2025\/?page_id=398"},"modified":"2025-03-05T14:24:57","modified_gmt":"2025-03-05T14:24:57","slug":"track-3","status":"publish","type":"page","link":"https:\/\/conferences.sigappfr.org\/aiccsa2025\/track-3\/","title":{"rendered":"Track 3: Data Science, knowledge engineering, and ontologies (including Information Retrieval, Big Data, Databases, and Knowledge Systems)"},"content":{"rendered":"\n

Track chairs:<\/strong>
Mohamed Reda Bouadjenek, Deakin University, Australia
reda.bouadjenek@deakin.edu.au<\/a>

Khouloud Boukadi, University of\u00a0Sfax, Tunisia
khouloud.boukadi@fsegs.usf.tn<\/a>

Haytham Elghazel, Universit\u00e9 Claude Bernard Lyon 1, France
haytham.elghazel@univ-lyon1.fr<\/a>

Scope<\/strong>
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<\/strong>
The topics of interest of the track include, but are not limited to:
\u2013 Data science
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Advanced data analytics
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Machine learning and data science ( Learning representations and features from data, Machine learning algorithms for large-scale content mining)
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Data cleaning
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Data visualization
\u2013 Information Search and Retrieval
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Query and document analysis, representation and understanding
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Web search models, and ranking
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Web recommender systems
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Evaluation methodologies and metrics
\u2013 Knowledge engineering, and ontologies
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Ontologies and semantics
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Techniques for the creation, curation, publication and consumption of Knowledge Graphs, including Methods for Developing and Maintaining Shared Vocabularies\/Ontologies
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Data Modeling and Inference
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Explanations and User-friendly Interaction
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Exploitation of Semantic Data for Machine Learning Tasks

Examples of application areas :<\/strong>
\u2022 Healthcare
\u2022 Social sciences
\u2022 Recommender platforms
\u2022 Logistics
\u2022 Transportation
\u2022 Urban planning
\u2022 Resource management (energy, water, air quality, waste management).<\/p><\/div><\/div>\n\n\n\n

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