  The proliferation of heterogeneous data sources of semantic knowledge base
intensifies the need of an automatic instance matching technique. However, the
efficiency of instance matching is often influenced by the weight of a property
associated to instances. Automatic weight generation is a non-trivial, however
an important task in instance matching technique. Therefore, identifying an
appropriate metric for generating weight for a property automatically is
nevertheless a formidable task. In this paper, we investigate an approach of
generating weights automatically by considering hypotheses: (1) the weight of a
property is directly proportional to the ratio of the number of its distinct
values to the number of instances contain the property, and (2) the weight is
also proportional to the ratio of the number of distinct values of a property
to the number of instances in a training dataset. The basic intuition behind
the use of our approach is the classical theory of information content that
infrequent words are more informative than frequent ones. Our mathematical
model derives a metric for generating property weights automatically, which is
applied in instance matching system to produce re-conciliated instances
efficiently. Our experiments and evaluations show the effectiveness of our
proposed metric of automatic weight generation for properties in an instance
matching technique.
