Garbage In, Garbage Out? An Empirical Look at Information Richness of LBD Input Types

Literature-Based Discovery (LBD) which is a sub-discipline of text mining, aims to detect meaningful implicit knowledge linkages in digital libraries that have the potential in generating novel research hypotheses. The input can be considered as one of the most critical components of the LBD process as the entire knowledge discovery is solely dependent on the content and quality of input. However, there is no uniform selection of the input since different LBD studies have picked different input types (e.g., titles, abstracts, keywords). This emphasises the need for assessing the information richness of inputs to decide the most suited input type for LBD workflow. Therefore, this study focuses on a large-scale assessment of the information richness of different variants of popular LBD input types. Our observations are consistent with all of the five golden test cases in the discipline.