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Classification of Figures in Biomedical Literature toward a Figure Finding System

As biomedical full-text papers are becoming more available in digitized form on-line, there is a need for tools to mine information from all parts in the papers. Notably, since figures and their legends/captions in biomedical papers provide important information about research outcomes, mining techniques targeting them have attracted a great deal of attention. However, even a simple-sounding task such as recognizing gene/protein and chemical compound names (named entities) in figures is very difficult, so the automatic extraction of all information in figures is a major challenge. Liu, et al. developed a figure legend indexing and classification system [1]. They defined schematic representations of protein interactions and signaling events in biomedical literature as an interesting figure type, and took a supervised machine learning approach to classify figures and performed indexing of figures by using figure legends. However, they did not utilize the main text referring to the figures, nor the contents of the figures that often contain more information than their legends. Shatkay, et al. used graphical features of images combining with the text of PubMed abstracts for biomedical document categorization [3]. They did not extract textual information in figures as well. Additionally, figure legends and the full-text were not used at all. Especially in biomedical papers, figures and text data contained in their legends and the main text tend to complement each other, so effective figure mining should be performed on all of them. Nevertheless, such an integrated mining system has not been achieved to our knowledge. Our goal is to build a figure finding system, which we call FigFinder, to retrieve figures relevant to a user’s query (gene/protein or chemical compound names) based on information contained in figures, their legends and the main text in an integrative manner.