  The search for relevant information can be very frustrating for users who,
unintentionally, use too general or inappropriate keywords to express their
requests. To overcome this situation, query expansion techniques aim at
transforming the user request by adding new terms, referred as expansion
features, that better describe the real intent of the users. We propose a
method that relies exclusively on relevant structures (as opposed to the use of
semantics) found in knowledge bases (KBs) to extract the expansion features. We
call our method Structural Query Expansion (SQE). The structural analysis of
KBs takes us to propose a set of structural motifs that connect their strongly
related entries, which can be used to extract expansion features. In this paper
we use Wikipedia as our KB, which is probably one of the largest sources of
information. SQE is capable of achieving more than 150% improvement over non
expanded queries and is able to identify the expansion features in less than
0.2 seconds in the worst case scenario. Most significantly, we believe that we
are contributing to open new research directions in query expansion, proposing
a method that is orthogonal to many current systems. For example, SQE improves
pseudo-relevance feedback techniques up to 13%
