@article{
 title = {Text mining: Generating hypotheses from MEDLINE},
 type = {article},
 year = {2004},
 identifiers = {[object Object]},
 pages = {396-413},
 volume = {55},
 websites = {http://doi.wiley.com/10.1002/asi.10389},
 id = {44dd00d2-1289-33c4-8937-62d3fe3f6c75},
 created = {2011-12-29T19:53:53.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Srinivasan2004},
 private_publication = {false},
 abstract = {Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience, and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection. In this report, we present open and closed text mining algorithms that are built within the discovery framework established by Swanson and Smalheiser. Our algorithms represent topics using metadata profiles. When applied to MEDLINE, these are MeSH based profiles. We present experiments that demonstrate the effectiveness of our algorithms. Specifically, our algorithms successfully generate ranked term lists where the key terms representing novel relationships between topics are ranked high.},
 bibtype = {article},
 author = {Srinivasan, Padmini},
 journal = {Journal of the American Society for Information Science and Technology},
 number = {5}
}

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