  Knowledge bases (KBs) contain plenty of structured world and commonsense
knowledge. As such, they often complement distributional text-based information
and facilitate various downstream tasks. Since their manual construction is
resource- and time-intensive, recent efforts have tried leveraging large
pretrained language models (PLMs) to generate additional monolingual knowledge
facts for KBs. However, such methods have not been attempted for building and
enriching multilingual KBs. Besides wider application, such multilingual KBs
can provide richer combined knowledge than monolingual (e.g., English) KBs.
Knowledge expressed in different languages may be complementary and unequally
distributed: this implies that the knowledge available in high-resource
languages can be transferred to low-resource ones. To achieve this, it is
crucial to represent multilingual knowledge in a shared/unified space. To this
end, we propose a unified representation model, Prix-LM, for multilingual KB
construction and completion. We leverage two types of knowledge, monolingual
triples and cross-lingual links, extracted from existing multilingual KBs, and
tune a multilingual language encoder XLM-R via a causal language modeling
objective. Prix-LM integrates useful multilingual and KB-based factual
knowledge into a single model. Experiments on standard entity-related tasks,
such as link prediction in multiple languages, cross-lingual entity linking and
bilingual lexicon induction, demonstrate its effectiveness, with gains reported
over strong task-specialised baselines.
