Semantic Web technologies have redefined and strengthened the Enterprise-Web interoperability over the last decade. Linked Open Data (LOD) refers to a set of best practices that empower enterprises to publish and interlink their data using existing ontologies on the World Wide Web. Current research in LOD focuses on expert search, the creation of unified information space and augmentation of core data from an enterprise context. However, existing approaches for publication of enterprise data as LOD are domain-specific, ad-hoc and suffer from lack of uniform representation across domains. The paper proposes a novel methodology called LinkED that contributes towards LOD literature in two ways: (a) streamlines the publishing process through five stages of cleaning, triplification, interlinking, storage and visualization; (b) addresses the latest challenges in LOD publication, namely: inadequate links, inconsistencies in the quality of the dataset and replicability of the LOD publication process. Further, the methodology is demonstrated via the publication of digital repository data as LOD in a university setting, which is evaluated based on two semantic standards: Five-Star model and data quality metrics. Overall, the paper provides a generic LOD publication process that is applicable across various domains such as healthcare, e-governance, banking, and tourism, to name a few.
All Science Journal Classification (ASJC) codes
- Computer Science(all)