Error Detection in a Large-Scale Lexical Taxonomy
  Knowledge base (KB) is an important aspect in artificial intelligence. One
significant challenge faced by KB construction is that it contains many noises,
which prevents its effective usage. Even though some KB cleansing algorithms
have been proposed, they focus on the structure of the knowledge graph and
neglect the relation between the concepts, which could be helpful to discover
wrong relations in KB. Motived by this, we measure the relation of two concepts
by the distance between their corresponding instances and detect errors within
the intersection of the conflicting concept sets. For efficient and effective
knowledge base cleansing, we first apply a distance-based Model to determine
the conflicting concept sets using two different methods. Then, we propose and
analyze several algorithms on how to detect and repairing the errors based on
our model, where we use hash method for an efficient way to calculate distance.
Experimental results demonstrate that the proposed approaches could cleanse the
knowledge bases efficiently and effectively.
