• DOI:10.1145/3209978.3210055
Corpus ID: 13748283

Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach

@article{Shen2018EntitySS,
  title={Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach},
  author={J. Shen and Jinfeng Xiao and Xinwei He and Jingbo Shang and Saurabh Sinha and Jiawei Han},
  journal={The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
  year={2018}
}

J. Shen, Jinfeng Xiao, +3 authors Jiawei Han

Published 2018

Computer Science

The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

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… CONTINUE READING

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