Francophone Days of Knowledge Engineering (IC)

July 1 - July 2 - July 3 - July 4


Knowledge engineering can be seen as the part of Artificial Intelligence that deals with knowledge from different points of view such as representation, acquisition and integration in numerical environments. Its aim is to produce methods and « intelligent » tools capable of helping humans in their activities.

The IC conference is a place dedicated to exchange, reflexion, presentation and confrontation of  theories, practices, methods and tools. This community is currently taking into consideration the growing interest for learning algorithms and their impact on individual and collective practices. An interlinked issue is the protection of privacy in knowledge engineering approaches that collect and process personal data. The guest speaker, Ruben Verborgh, will address this theme in his talk "How Solid aims to impact the Web (and AI with it) ».

It is important to note that for this edition of the conference a session will be co-organized with JSAI (Japanese Society for Artificial Intelligence). Authors can thus submit their articles in English.

Themes of the conference

Submissions dealing with « personal data and privacy in knowledge engineering » are specially welcome. It is also possible to present original work with a theoretical, methodological or practical dimension dealing with one of following themes (non exhaustive list) :

Knowledge representation, ontologies

  • Knowledge models: design, evolution, evaluation, use, life cycle.
  • Modeling and formalization: formal and informal models, standardization
  • Methods and tools for ontology engineering: alignment, integration, modularity, fusion, metrics, design patterns, visualization
  • Design and reuse of top-level, core-domain and domain ontologies, interoperability, terminologies

From data to knowledge

  • Extraction and acquisition of knowledge, ontology population, semantic annotation
  • Knowledge acquisition from texts, images, non structured data, interactions
  • Collaborative system engineering, crowd-sourcing
  • Process and reasoning with knowledge
  • Knowledge engineering and data mining

Knowledge engineering for the Web

  • Storing and querying distributed knowledge
  • Semantic Web, Web of Data, social Web, Web of Things

Data and knowledge

  • Knowledge engineering and complex data : multimedia, multilingualism, temporal spacial and multi scale data, imprecise and uncertain data
  • Security in knowledge base systems
  • Provenance and confidence in data
  • Metrics and evaluation approaches for data and knowledge quality

Reasoning and machine learning

  • Inferences and business rules
  • Logical reasoning, approximation, statistical reasoning, analogical reasoning, case based reasoning, reasoning with non classic logics
  • Deep learning and knowledge graphs
  • Graph embedding of knowledge graphs

Knowledge engineering applications and feedback

  • Information retrieval and indexation
  • Human Machine interaction: data, knowledge and interconnection visualization, knowledge based system interface, explanation
  • Multi-agent system
  • Chatbot
  • Recommendation systems based on knowledge
  • Adaptation, personalization: user profiles, context and adaptation models, sentiment analysis
  • Processing big and heterogeneous data
  • Applications to life sciences, agriculture, culture, education, industry, economics, law, business intelligence, etc.