Finding reusable structured resources for the integration of environmental research data

Successful data integration requires careful examination of data semantics, a task that has often been approached with the use of ontologies. However, there are some barriers to build ontologies for data integration in complex domains such as the environmental one. A relevant problem is the development of new ontologies disregarding previous knowledge resources such as reference models and vocabularies. This paper addresses this challenge by proposing a systematic approach (dubbed CLeAR) for the identification and selection of reusable artifacts for building ontologies with the purpose of research data integration. CLeAR follows some principles of the systematic literature reviews, supporting the search for structured resources in the scientific literature. We apply CLeAR to the environmental domain. A total of 543 publications were surveyed. The results obtained provide a set of 75 structured resources for the environmental domain, evaluated according domain coverage and some quality attributes (e.g., proper documentation, community acceptance).