  Open Information Extraction (OIE) methods extract a large number of OIE
triples (noun phrase, relation phrase, noun phrase) from text, which compose
large Open Knowledge Bases (OKBs). However, noun phrases (NPs) and relation
phrases (RPs) in OKBs are not canonicalized and often appear in different
paraphrased textual variants, which leads to redundant and ambiguous facts. To
address this problem, there are two related tasks: OKB canonicalization (i.e.,
convert NPs and RPs to canonicalized form) and OKB linking (i.e., link NPs and
RPs with their corresponding entities and relations in a curated Knowledge Base
(e.g., DBPedia). These two tasks are tightly coupled, and one task can benefit
significantly from the other. However, they have been studied in isolation so
far. In this paper, we explore the task of joint OKB canonicalization and
linking for the first time, and propose a novel framework JOCL based on factor
graph model to make them reinforce each other. JOCL is flexible enough to
combine different signals from both tasks, and able to extend to fit any new
signals. A thorough experimental study over two large scale OIE triple data
sets shows that our framework outperforms all the baseline methods for the task
of OKB canonicalization (OKB linking) in terms of average F1 (accuracy).
