Recent Advances in Literature Based Discovery
Literature Based Discovery (LBD) is a process that searches for hidden and important connections among information embedded in published literature. Employing techniques from Information Retrieval and Natural Language Processing, LBD has potential for widespread application yet is currently implemented primarily in the medical domain. This article examines several published LBD systems, comparing their descriptions of domain and input data, techniques to locate important concepts from text, models of discovery, experimental results, visualizations, and evaluation of the results. Since there is no comprehensive âgold standard, â or consistent formal evaluation methodology for LBD systems, the development and usage of effective metrics for such systems is also discussed, providing several options. Also, since LBD is currently often time-intensive, requiring human input at one or more points, a fully-automated system will enhance the efficiency of the process. Therefore, this article considers methods for automated systems based on data mining.