Books

This highly practical handbook teaches you how to unlock the value of your existing metadata through cleaning, reconciliation, enrichment and linking and how to streamline the process of new metadata creation.

Metadata experts Seth van Hooland and Ruben Verborgh introduce the key concepts of metadata standards and Linked Data and how they can be practically applied to existing metadata, giving readers the tools and understanding to achieve maximum results with limited resources. Readers will learn how to critically assess and use (semi-)automated methods of managing metadata through hands-on exercises within the book and on the accompanying website. Each chapter is built around a case study from institutions around the world, demonstrating how freely available tools are being successfully used in different metadata contexts.

Data is supposed to be the new gold, but how can you unlock the value in your data? Managing large datasets used to be a task for specialists, but you don’t have to worry about inconsistencies or errors anymore. OpenRefine lets you clean, link, and publish your dataset in a breeze. Using OpenRefine takes you on a practical tour of all the features of this well-known data transformation tool. It is a hands-on recipe book that teaches you data techniques by example. Starting from the basics, it gradually transforms you into an OpenRefine expert.

This book will teach you all the necessary skills to handle any large dataset and to turn it into high-quality data for the Web. After you learn how to analyze data and spot issues, we’ll see how we can solve them to obtain a clean dataset. Messy and inconsistent data is recovered through advanced techniques such as automated clustering. We’ll then show extract links from keyword and full-text fields using reconciliation and named-entity extraction. Using OpenRefine is more than a manual: it’s a guide stuffed with tips and tricks to get the best out of your data.

Ever since its creation at the end of the 20^th century, the Web has profoundly shaped the world’s information flow. Nowadays, the Web’s consumers no longer consist of solely people, but increasingly of machine clients that have been instructed to perform tasks for people. Lacking the ability to interpret natural language, machine clients need a more explicit means to decide what steps they should take. This thesis investigates the obstacles for machines on the current Web, and provides solutions that aim to improve the autonomy of machine clients. In addition, we will enhance the Web’s linking mechanism for people, to enable serendipitous reuse of data between Web applications that were not connected previously.