  In sponsored search, retrieving synonymous keywords for exact match type is
important for accurately targeted advertising. Data-driven deep learning-based
method has been proposed to tackle this problem. An apparent disadvantage of
this method is its poor generalization performance on entity-level long-tail
instances, even though they might share similar concept-level patterns with
frequent instances. With the help of a large knowledge base, we find that most
commercial synonymous query-keyword pairs can be abstracted into meaningful
conceptual patterns through concept tagging. Based on this fact, we propose a
novel knowledge-driven conceptual retrieval framework to mitigate this problem,
which consists of three parts: data conceptualization, matching via conceptual
patterns and concept-augmented discrimination. Both offline and online
experiments show that our method is very effective. This framework has been
successfully applied to Baidu's sponsored search system, which yields a
significant improvement in revenue.
