  Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written
in a language different from the user's query. The intrinsic gap between
different languages is an essential challenge for CLIR. In this paper, we
introduce the multilingual knowledge graph (KG) to the CLIR task due to the
sufficient information of entities in multiple languages. It is regarded as a
"silver bullet" to simultaneously perform explicit alignment between queries
and documents and also broaden the representations of queries. And we propose a
model named CLIR with hierarchical knowledge enhancement (HIKE) for our task.
The proposed model encodes the textual information in queries, documents and
the KG with multilingual BERT, and incorporates the KG information in the
query-document matching process with a hierarchical information fusion
mechanism. Particularly, HIKE first integrates the entities and their
neighborhood in KG into query representations with a knowledge-level fusion,
then combines the knowledge from both source and target languages to further
mitigate the linguistic gap with a language-level fusion. Finally, experimental
results demonstrate that HIKE achieves substantial improvements over
state-of-the-art competitors.
