Linked Data Application Development Methodology

Abstract

The vast amount of data available over the distributed infrastructure of the Web has initiated the development of techniques for their representation, storage and usage. One of these techniques is the Linked Data paradigm, which aims to provide unified practices for publishing and contextually interlinking data on the Web, by using the World Wide Web Consortium (W3C) standards and the Semantic Web technologies. This approach enables the transformation of the Web from a web of documents, to a web of data. With it, the Web transforms into a distributed network of data which can be used by software agents and machines. The interlinked nature of the distributed datasets enables the creation of advanced use-case scenarios for the end users and their applications , scenarios previously unavailable over isolated data silos. This creates opportunities for generating new business values in the industry. The adoption of the Linked Data principles by data publishers from the research community and the industry has led to the creation of the Linked Open Data (LOD) Cloud, a vast collection of interlinked data published on and accessible via the existing infrastructure of the Web. The experience in creating these Linked Data datasets has led to the development of a few methodo-logies for transforming and publishing Linked Data. However, even though these methodologies cover the process of modeling, transforming / generating and publishing Linked Data, they do not consider reuse of the steps from the life-cycle. This results in separate and independent efforts to generate Linked Data within a given domain, which always go through the entire set of life-cycle steps. In this PhD thesis, based on our experience with generating Linked Data in various domains and based on the existing Linked Data methodologies, we define a new Linked Data methodology with a focus on reuse. It consists of five steps which encompass the tasks of studying the domain, modeling the data, transforming the data, publishing it and exploiting it. In each of the steps, the methodology provides guidance to data publishers on defining reusable components in the form of tools, schemas and services, for the given domain. With this, future Linked Data publishers in the domain would be able to reuse these components to go through the life-cycle steps in a more efficient and productive manner. With the reuse of schemas from the domain, the resulting Linked Data dataset will be compatible and aligned with other datasets generated by reusing the same components, which additionally leverages the value of the datasets. This approach aims to encourage data publishers to generate high-quality, aligned Linked Data datasets from various domains, leading to further growth of the number of datasets on the LOD Cloud, their quality and the exploitation scenarios. With the emergence of data-driven scientific fields, such as Data Science, creating and publishing high-quality Linked Data datasets on the Web is becoming even more important, as it provides an open dataspace built on existing Web standards. Such a dataspace enables data scientists to make data analytics over the cleaned, structured and aligned data in it, in order to produce new knowledge and introduce new value in a given domain. As the Linked Data principles are also applicable within closed environments over proprietary data, the same methods and approaches are applicable in the enterprise domain as well.