Automatic figure ranking and user interfacing for intelligent figure search

• Published in: PloS one 2010-10, Vol.5 (10), p.e12983-e12983
• Main Authors: Yu, Hong, Liu, Feifan, Ramesh, Balaji Polepalli
• Format: Article
• Language: English
• Subjects: Access to information; Algorithms; Annotations; Artificial intelligence; Automation; Biotechnology; Classification; Computational Biology/Genomics; Computational Biology/Literature Analysis; Computer engineering; Computer science; Computer Science/Applications; Computer Science/Information Technology; Electrical engineering; Evaluation; Gene expression; Health informatics; Hypotheses; Hypothesis testing; Information processing; Interfaces; Machine translation; Medical research; Models, Theoretical; Natural language processing; Ranking; Ratings & rankings; Researchers; Search engines; Searching; User interface; User interfaces; User-Computer Interface
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0012983

Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org). Existing research in figure search treats each figure equally, but we introduce a novel concept of "figure ranking": figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery. We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs) incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation. The evaluation results conclude that automatic figure ranking and user interfacing as we reported in this study can be fully implemented in online publishing. The novel user interface integrated with the automatic figure ranking system provides a more efficient and robust way to access scientific information in the biomedical domain, which will further enhance our existing figure search engine to better facilitate accessing figures of interest for bioscientists.