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Knowledge and Information Fusion

W Liu, D Dubois, D. Bell, L Godo

In large scale, distributed data-driven and knowledge-driven intelligent systems, modelling and managing heterogeneous information from multiple sources and making use of experts/domain knowledge to assist decision making faces many challenges. These challenges include:

  1. Modelling: how to determine and select adequate theories/ formalisms to represent  a variety of data, information, and knowledge; reasoning with such data/information and knowledge;
  2. Uncertainty/inconsistency: how to handle uncertainty, reliability, incompleteness, and inconsistency in data/information and knowledge;
  3. Fusion: how to merge or combining information and knowledge provided by multiple sources; the aim of such a fusion is to lay bare the reliable part of the available data, taking into account possible inconsistencies, while not introducing artificial precision.
  4. Change: how to reflect current available information in a dynamic environment by revising or updating agents' beliefs and knowledge : an intelligent system should account for the flux of new evidence, and learn from it;
  5. System development: how to design and evaluate robust, scalable, and fault tolerant  computerized systems based on theoretical development in the above areas to meet the requirements of real-world applications.


Research in the Knowledge and Information Fusion group in KDE has been primarily carried out  to meet these challenges.

Our research starts with establishing sound theoretical foundations for modelling and managing heterogeneous information and knowledge.  
This includes

  1. examining and comparing different information/knowledge representation theories, especially under uncertainty and inconsistency;
  2. discovering general principles governing the fusion of information/ knowledge, regardless of what theory is chosen to model such information or knowledge;
  3. developing fusion or merging algorithms and strategies for combining information and knowledge from different sources;
  4. developing revision or updating strategies in dynamic systems to take into account new information or the changes of environment, or newly discovered knowledge.


We actively apply our theoretical findings to real-world applications.  
Application areas include belief fusion and revision in multi-agent systems, sensor information fusion and event reasoning in static and mobile environment for decision making in large sensor networks (especially for physical/cyber security, infrastructure security, and connected health), knowledge-driven risk analysis and requirement decisions in manufacturing processes, and semi-structured data fusion.


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