Automatic Preservation Watch using Information Extraction on the Web

Luís Faria, Marcel Ras, Alan Akbik, Miguel Ferreira, Barbara Sierman and José Carlos Ramalho:

Automatic Preservation Watch using Information Extraction on the Web

In: IPRES 2013 – Proceedings of the 10th International Conference on Preservation of Digital Objects ; ed. José Borbinha, Michael Nelson, Steve Knight, http://purl.pt/24107, ISBN 978-972-565-493-4

Abstract:

The ability to recognise when digital content is becoming endangered is essential for maintaining the long-term, continuous and authentic access to digital assets. To achieve this ability, knowledge about aspects of the world that might hinder the preservation of content is needed. However, the processes of gathering, managing and reasoning on knowledge can become manually infeasible when the volume and heterogeneity of content increases, multiplying the aspects to monitor. Automation of these processes is possible, but its usefulness is limited to the data it is able to gather. Up to now, automatic digital preservation processes have been restricted to knowledge expressed in a machine understandable language, ignoring a plethora of data expressed in natural language, such as the DPC Technology Watch Reports, which could greatly contribute to the completeness and freshness of data about aspects of the world related to digital preservation.

This paper presents a real case scenario from the National Library of the Netherlands, where the monitoring of publishers and journals is needed. This knowledge is mostly represented in natural language on Web sites of the publishers and, therefore, is difficult to automatically monitor. In this paper, we demonstrate how we use information extraction technologies to find and extract machine readable information on publishers and journals for ingestion into automatic digital preservation watch tools. We show that the results of automatic semantic extraction are a good complement to existing knowledge bases on publishers, finding newer and more complete data. We demonstrate the viability of the approach as an alternative or auxiliary method for automatically gathering information on preservation risks in digital content.

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