Affiliations: [a] Institute of Computing, University of Campinas, SP, Brazil. E-mails: [email protected], [email protected] | [b] UNIFACCAMP and Center for Information Technology Renato Archer, SP, Brazil. E-mail: [email protected] | [c] Central Queensland University of Technology, Melbourne, Australia. E-mail: [email protected] | [d] Nucleus of Informatics Applied to Education, University of Campinas, SP, Brazil | [e] Swinburne University of Technology, Melbourne, Australia. E-mail: [email protected]
Abstract: RDF data has been extensively deployed describing various types of resources in a structured way. Links between data elements described by RDF models stand for the core of Semantic Web. The rising amount of structured data published in public RDF repositories, also known as Linked Open Data, elucidates the success of the global and unified dataset proposed by the vision of the Semantic Web. Nowadays, semi-automatic algorithms build connections among these datasets by exploring a variety of methods. Interconnected open data demands automatic methods and tools to maintain their consistency over time. The update of linked data is considered as key process due to the evolutionary characteristic of such structured datasets. However, data changing operations might influence well-formed links, which turns difficult to maintain the consistencies of connections over time. In this article, we propose a thorough survey that provides a systematic review of the state of the art in link maintenance in linked open data evolution scenario. We conduct a detailed analysis of the literature for characterising and understanding methods and algorithms responsible for detecting, fixing and updating links between RDF data. Our investigation provides a categorisation of existing approaches as well as describes and discusses existing studies. The results reveal an absence of comprehensive solutions suited to fully detect, warn and automatically maintain the consistency of linked data over time.
Keywords: Link integrity, link maintenance, RDF evolution
Journal: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-25, 2020