  Automated completion of open knowledge bases (Open KBs), which are
constructed from triples of the form (subject phrase, relation phrase, object
phrase), obtained via open information extraction (Open IE) system, are useful
for discovering novel facts that may not be directly present in the text.
However, research in Open KB completion (Open KBC) has so far been limited to
resource-rich languages like English. Using the latest advances in multilingual
Open IE, we construct the first multilingual Open KBC dataset, called mOKB6,
containing facts from Wikipedia in six languages (including English). Improving
the previous Open KB construction pipeline by doing multilingual coreference
resolution and keeping only entity-linked triples, we create a dense Open KB.
We experiment with several models for the task and observe a consistent benefit
of combining languages with the help of shared embedding space as well as
translations of facts. We also observe that current multilingual models
struggle to remember facts seen in languages of different scripts.
