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

Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings