Link maintenance for integrity in linked open data evolution: Literature survey and open challenges

Tracking #: 2255-3468

This paper is currently under review
Authors: 
Andre Regino
Julio Kiyoshi Rodrigues
Julio Cesar dos Reis
Rodrigo Bonacin
Ahsan Morshed1
Timos Sellis1

Responsible editor: 
Oscar Corcho

Submission type: 
Survey Article
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 similarity computation methods. Interconnected open data demands automatic methods and tools to maintain their consistency over time. The update of linked data is considered an key process due to the evolutionary characteristic of these structured datasets. However, data changing operations might influence well-formed links, which turns difficult to maintain the consistencies of connections over time. In this paper, 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 structured 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.
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