  Expert finding is an important task in both industry and academia. It is
challenging to rank candidates with appropriate expertise for various queries.
In addition, different types of objects interact with one another, which
naturally forms heterogeneous information networks. We study the task of expert
finding in heterogeneous bibliographical networks based on two aspects: textual
content analysis and authority ranking. Regarding the textual content analysis,
we propose a new method for query expansion via locally-trained embedding
learning with concept hierarchy as guidance, which is particularly tailored for
specific queries with narrow semantic meanings. Compared with global embedding
learning, locally-trained embedding learning projects the terms into a latent
semantic space constrained on relevant topics, therefore it preserves more
precise and subtle information for specific queries. Considering the candidate
ranking, the heterogeneous information network structure, while being largely
ignored in the previous studies of expert finding, provides additional
information. Specifically, different types of interactions among objects play
different roles. We propose a ranking algorithm to estimate the authority of
objects in the network, treating each strongly-typed edge type individually. To
demonstrate the effectiveness of the proposed framework, we apply the proposed
method to a large-scale bibliographical dataset with over two million entries
and one million researcher candidates. The experiment results show that the
proposed framework outperforms existing methods for both general and specific
queries.
