Uncertain knowledge association through information gain

The problem of entity association is at the core of information mining techniques. In this work we propose an approach that links the similarity of two knowledge entities to the effort required to fuse them in one. This is implemented as an iterative updating process. It unites an evolving initial knowledge entity and a piece of new information, which is repeatedly in-corporated, until a convergence state is reached. The number of updating repetitions can be used as an importance index qualifying the new evi-dence.