  Query understanding plays a key role in exploring users' search intents and
facilitating users to locate their most desired information. However, it is
inherently challenging since it needs to capture semantic information from
short and ambiguous queries and often requires massive task-specific labeled
data. In recent years, pre-trained language models (PLMs) have advanced various
natural language processing tasks because they can extract general semantic
information from large-scale corpora. Therefore, there are unprecedented
opportunities to adopt PLMs for query understanding. However, there is a gap
between the goal of query understanding and existing pre-training strategies --
the goal of query understanding is to boost search performance while existing
strategies rarely consider this goal. Thus, directly applying them to query
understanding is sub-optimal. On the other hand, search logs contain user
clicks between queries and urls that provide rich users' search behavioral
information on queries beyond their content. Therefore, in this paper, we aim
to fill this gap by exploring search logs. In particular, to incorporate search
logs into pre-training, we first construct a query graph where nodes are
queries and two queries are connected if they lead to clicks on the same urls.
Then we propose a novel graph-enhanced pre-training framework, GE-BERT, which
can leverage both query content and the query graph. In other words, GE-BERT
can capture both the semantic information and the users' search behavioral
information of queries. Extensive experiments on various query understanding
tasks have demonstrated the effectiveness of the proposed framework.
