Information Extraction in Digital Libraries: First Steps towards Portability of LBD Workflow

Literature-Based Discovery (LBD) refers to the process of detecting implicit, novel knowledge linkages hidden in scientific digital libraries and its contribution in accelerating research innovations is widely recognised. Despite significant advances, almost all the prior research efforts suffer from a major research deficiency which is lack of portability. That is, the existing LBD models are highly dependent on specialised and domain-dependent knowledge resources that restrict their applicability to limited problem areas or domains. However, LBD, the process of discovering new knowledge from unstructured text that potentially leads to novel research innovations is crucial despite the domain. Thus, this study proposes an interdisciplinary LBD framework by circumventing the existing impediments in the LBD workflow towards promoting portable scientific problem solving. To this end, we considered the revolutionary opportunities offered through Semantic Web by employing DBpedia for the first time in the LBD workflow. The suitability of our proposals to overcome the prevailing limitations are evaluated by comparing them with commonly used domain specific resources.