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.
Connections among RDF (Resource Description Framework) data elements represent the core of LOD (Linked Open Data). These connections are built with semi-automatic linking algorithms using a variety of similarity methods. Interconnected data demand automatic methods to maintain their consistency. Constant update of RDF connections is relevant for the evolution of RDF datasets. However, changing operations can influence well-formed links, which turns difficult the consistency of the connections over time. This study investigated new methods responsible for fixing and updating links among structured data following ontologies rules and properties. We contribute with the design and development of an automatic method that updates RDF links based on changing operations in RDF datasets. The framework that implements our method - named LODMF - was evaluated in terms of discovering broken links in big and well-known Linked Open datasets.
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