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ACL-SRW 2018: Automatic Detection of Cross-Disciplinary Knowledge Associations

Congratulations to Menasha Thilakaratne for having her thesis work accepted for the Student Research Workshop at ACL 2018!!

M. Thilakaratne, K. Falkner and T. Atapattu. Automatic Detection of Cross-Disciplinary Knowledge Associations. Accepted for Student Research Workshop at ACL 2018.

Detecting interesting, cross-disciplinary knowledge associations hidden in scientific publications can greatly assist scientists to formulate and validate scientifically sensible novel research hypotheses. This will also introduce new areas of research that can be successfully linked with their research discipline. Currently, this process is mostly performed manually by exploring the scientific publications, requiring a substantial amount of time and effort. Due to the exponential growth of scientific literature, it has become almost impossible for an individual scientist to keep track of all research advances. As a result, scientists tend to deal with fragments of the literature according to their specialisation. Consequently, important, hidden associations among these fragmented knowledge that can be linked to produce significant scientific discoveries remain unnoticed. This study aims to develop a novel knowledge discovery approach that suggests most promising research pathways that have the potential to lead to novel scientific innovations by analysing existing literature.

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