Are Abstracts Enough for Hypothesis Generation?
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The potential for automatic hypothesis generation (HG) systems to improve research productivity keeps pace with the growing set of publicly available scientific information. But as data becomes easier to acquire, we must understand the effect different textual data sources have on our resulting hypotheses. Are abstracts enough for HG, or does it need full-text papers? How many papers does an HG system need to make valuable predictions? What effect do corpus size and document length have on HG results? To answer these questions we train multiple versions of knowledge network-based HG system, Moliere, on varying corpora in order to compare challenges and tradeoffs in terms of result quality and computational requirements. Moliere generalizes main principles of similar knowledge network-based HG systems and reinforces them with topic modeling components. The corpora include the abstract and full-text versions of PubMed Central, as well as iterative halves of MEDLINE, which allows us to compare the effect document length and count has on the results. We find that corpora with a higher median document length result in higher quality results, yet require substantially longer to process. Additionally, we find that the effect of document length is greater than that of document count, even if both sets contain only paper abstracts. Reproducibility: Our code can be found at github.com/JSybrandt/moliere, and our data is hosted at bit.ly/2GxghpM.
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