Book Image

Using OpenRefine

Book Image

Using OpenRefine

Overview of this book

Data today is like gold - but how can you manage your most valuable assets? Managing large datasets used to be a task for specialists, but the game has changed - data analysis is an open playing field. Messy data is now in your hands! With OpenRefine the task is a little easier, as it provides you with the necessary tools for cleaning and presenting even the most complex data. Once it's clean, that's when you can start finding value. Using OpenRefine takes you on a practical and actionable through this popular data transformation tool. Packed with cookbook style recipes that will help you properly get to grips with data, this book is an accessible tutorial for anyone that wants to maximize the value of their data. 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.
Table of Contents (13 chapters)
Using OpenRefine
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Recipe 4 – reconciling with Linked Data


In the previous recipe, we talked about RDF and SPARQL without sketching the broader context in which these technologies were created, so let's introduce them now. Around the year 2000, web researchers and engineers were noticing that humans were no longer the only consumers of the Web; more and more machine clients, and thus pieces of software, started using the Web for various purposes. However, every such piece of software had to be hardcoded for a particular task, and they could not parse the natural language in documents on the human Web. Therefore, a vision called the Semantic Web was coined, a Web in which information would also be interpretable for machines. This was the start of RDF and SPARQL.

However, the vision was rather abstract and difficult to many people. Several of the concepts relied on concepts such as ontologies and reasoning, which can become very complex rapidly. Tim Berners-Lee, inventor of the Web and one of the creators of...