  To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a
more general task is to enrich a KG using triples from an open corpus, where
the obtained triples contain noisy entities and relations. It is challenging to
enrich a KG with newly harvested triples while maintaining the quality of the
knowledge representation. This paper proposes a system to refine a KG using
information harvested from an additional corpus. To this end, we formulate our
task as two coupled sub-tasks, namely join event extraction (JEE) and knowledge
graph fusion (KGF). We then propose a Collaborative Knowledge Graph Fusion
Framework to allow our sub-tasks to mutually assist one another in an
alternating manner. More concretely, the explorer carries out the JEE
supervised by both the ground-truth annotation and an existing KG provided by
the supervisor. The supervisor then evaluates the triples extracted by the
explorer and enriches the KG with those that are highly ranked. To implement
this evaluation, we further propose a Translated Relation Alignment Scoring
Mechanism to align and translate the extracted triples to the prior KG.
Experiments verify that this collaboration can both improve the performance of
the JEE and the KGF.
