Linked Data

The European Library and Europeana have both an extensive experience in aggregating metadata for bibliographical records or digital resources from the cultural heritage institutions of Europe. For both of them meeting the challenge is offered by multilingual and heterogeneous data is an ongoing effort.
The growth of the Semantic Web and the more generalised publication of knowledge organisation systems as linked open data offer the possibility to make these services truly multilingual.

While the work on linking our metadata continues, the library’s Research department is currently involved in an effort to link named entities in our full text collections. The purpose of this project is to contribute to a fully Linked Open Data-enabled library, entailing an enhanced user experience and improved discovery based on semantic relations. To further contribute to the progress of the semantic web, we will offer our full text enrichments as open data.

Abstract

The sea of information available to scholars and researchers can seem impossibly vast. But, with the help of a new grant, Cornell University Library, in collaboration with the Harvard Library Innovation Lab and Stanford University Libraries, aims to make that sea easier to navigate.
The Andrew W. Mellon Foundation’s Scholarly Communications and Information Technology Program awarded a nearly $1 million, two-year grant to Cornell to support an investigation using Linked Data and the Semantic Web to improve discovery and access of scholarly information by the three libraries.

The library and cultural institution communities have generally accepted the vision of moving to a Linked Data environment that will align and integrate their resources with those of the greater Semantic Web. But moving from vision to implementation is not easy or well-understood. A number of institutions have begun the needed infrastructure and tools development with pilot projects to provide structured data in support of discovery and navigation services for their collections and resources.

The 3rd ESWC Summer School will run in southern Crete from the morning of Monday September 2nd to midday Saturday September 7th 2013. The overall goal for this event is to provide intensive training and networking opportunities for the next EWC generation. In particular we wish to facilitate the creation of a new cohort of ESWC: Master's, Ph.D. students and junior researchers who will in time become the next leaders of the ESWC conference and of the Semantic Web research area in general.

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.

As part of our ongoing series of insights discussions with individuals doing innovative work related to digital preservation and stewardship I am excited to talk with Brian Schmidt. Brian works as an astronomer at the Research School of Astronomy and Astrophysics at the Australian National University and his research is based on a lot of the “big data” that many individuals in the digital preservation and stewardship community have been keenly interested in.

Harnessing Apache Mahout to Link Content - The National Library Board of Singapore has successfully used Apache Mahout to link contents in several collections such as its Infopedia collection of articles (http://infopedia.nl.sg). This article introduces Apache Mahout (http://mahout.apache.org) and focuses on its ability to link content through text analytic techniques. The article will run through the what, why, and the how.

This paper presents an approach for the automatic estima- tion of preservation risks for file formats. The main con- tribution 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 auto- matically aggregated from linked open data repositories as the basis for a risk analysis in the digital preservation do- main. This method is meant to facilitate decision making with regard to preservation of digital content in libraries and archives.