  While most of the knowledge bases already support the English language, there
is only one knowledge base for the Persian language, known as FarsBase, which
is automatically created via semi-structured web information. Unlike English
knowledge bases such as Wikidata, which have tremendous community support, the
population of a knowledge base like FarsBase must rely on automatically
extracted knowledge. Knowledge base population can let FarsBase keep growing in
size, as the system continues working. In this paper, we present a knowledge
base population system for the Persian language, which extracts knowledge from
unlabeled raw text, crawled from the Web. The proposed system consists of a set
of state-of-the-art modules such as an entity linking module as well as
information and relation extraction modules designed for FarsBase. Moreover, a
canonicalization system is introduced to link extracted relations to FarsBase
properties. Then, the system uses knowledge fusion techniques with minimal
intervention of human experts to integrate and filter the proper knowledge
instances, extracted by each module. To evaluate the performance of the
presented knowledge base population system, we present the first gold dataset
for benchmarking knowledge base population in the Persian language, which
consisting of 22015 FarsBase triples and verified by human experts. The
evaluation results demonstrate the efficiency of the proposed system.
