Imperfect Information

Most information systems are specifically designed for managing perfect information. However, real-world information is often imperfect. In fact human perception and acting often do not require exact numbers and facts. Consequently, a lot of useful information available in emails, documents, memos, etc. is inherently imperfect. Data imperfection can be caused by imprecision, vagueness, uncertainty, incompleteness or inconsistency.

In our research we investigate computational intelligence techniques, based on fuzzy logic and fuzzy set theory, that allow to model and handle data imperfections as adequate as possible without causing information loss. Herewith, we aim to make information management and processing more human-centric by better reflecting human knowledge and by better informing users about the veracity of their data.