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Semantic Association for Literature Based Discovery

This research explores the effectiveness of association measures at ranking the output terms of a literature based discovery (LBD) system. The amount of biomedical text published is growing exponentially and researchers are finding it increasingly difficult to keep up with new findings, even inside their area of expertise. LBD attempts to address this situation by automatically uncovering new, potentially meaningful relations between terms that could lead to new discoveries. A core challenge of LBD is that systems generate more potential discoveries than can be analyzed by a human, therefore effective ranking measures are essential.