  Understanding searchers' queries is an essential component of semantic search
systems. In many cases, search queries involve specific attributes of an entity
in a knowledge base (KB), which can be further used to find query answers. In
this study, we aim to move forward the understanding of queries by identifying
their related entity attributes from a knowledge base. To this end, we
introduce the task of entity attribute identification and propose two methods
to address it: (i) a model based on Markov Random Field, and (ii) a learning to
rank model. We develop a human annotated test collection and show that our
proposed methods can bring significant improvements over the baseline methods.
