On Using Declarative Generation Rules To Deliver Linked Biodiversity Data

Abstract

In the last decade, our capability to collect data has been improved significantly. A new era of big data has emerged as indicated by five characteristics of data: volume, variety, veracity, velocity, and value. The adoption of open-science approach is important in order to manage and exploit the available data appropriately. An open science approach enables curation, discovery, linking, and reusability of data across the globe. The challenge lies in the data heterogeneity, limitation of data interface, and conventional data visualization practices. In this work, we introduce a solution to overcome the challenges by using the Linked Data approach. The solution enables data to be represented in a machine-readable format and linked to or from external data sets, in a way that can be easily integrated, allow search optimization, as well as open the possibility to obtain new knowledge. Our solution consists of a schema construction to uniformly represent biodiversity data (mostly biological specimen data). After that, mapping rules were defined to align data from multiple biodiversity information systems that are available on the Web to the constructed schema. Finally, an engine will consume the mapping rules and generate linked data in a common format. Our results indicate that despite multiple data structures have been utilized by multiple systems, the mapping rules provide flexibility to accommodate every one of them. Further, we successfully demonstrated the possibility to deliver linked biodiversity data across multiple sources as our first step to harness big data biodiversity.