  Literature search is critical for any scientific research. Different from Web
or general domain search, a large portion of queries in scientific literature
search are entity-set queries, that is, multiple entities of possibly different
types. Entity-set queries reflect user's need for finding documents that
contain multiple entities and reveal inter-entity relationships and thus pose
non-trivial challenges to existing search algorithms that model each entity
separately. However, entity-set queries are usually sparse (i.e., not so
repetitive), which makes ineffective many supervised ranking models that rely
heavily on associated click history. To address these challenges, we introduce
SetRank, an unsupervised ranking framework that models inter-entity
relationships and captures entity type information. Furthermore, we develop a
novel unsupervised model selection algorithm, based on the technique of
weighted rank aggregation, to automatically choose the parameter settings in
SetRank without resorting to a labeled validation set. We evaluate our proposed
unsupervised approach using datasets from TREC Genomics Tracks and Semantic
Scholar's query log. The experiments demonstrate that SetRank significantly
outperforms the baseline unsupervised models, especially on entity-set queries,
and our model selection algorithm effectively chooses suitable parameter
settings.
