A Risk Analysis of File Formats for Preservation Planning

This paper presents an approach for the automatic estimation of preservation risks for file formats. The main contribution of this work is the definition of risk factors with associated severity levels and their automatic computation. Our goal is to make use of a solid knowledge base automatically aggregated from linked open data repositories as the basis for a risk analysis in the digital preservation domain. This method is meant to facilitate decision making with regard to preservation of digital content in libraries and archives. We have developed a tool for aggregating rich and trusted file format descriptions. It exploits available linked data resources and uses expert models to infer knowledge regarding the long-term preservation of digital content. The ontology mapping technique is employed for collecting the information from the web of linked data and integrating it in a common representation. Furthermore, we employ AI techniques (i.e. expert rules, clustering) for inferring explicit knowledge on the nature and preservation-friendliness of the file formats. A statistical analysis of the aggregated information and the qualitative analysis of the aggregated knowledge are presented in the evaluation part of the paper. A Web service is created to support programmatic access to format and risk analysis reports.