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

Chapter 4. Linking Datasets

Your dataset is not an island. Somewhere, related datasets exist, even in places where you might not expect them. For instance, if your dataset has a Country of Origin column, then it is related to a geographical database that lists the total area per country. An Author column in a book dataset relates to a list of authors with biographical data. All datasets have such connections, yet you might not know about them, and neither does the computer which contains your dataset. For instance, the record for The Picture of Dorian Gray might list Wilde, O . as its author, whereas a biographical dataset might only have an entry for Oscar Wilde. Even though they point to the same person, the string values are different, and it is thus difficult to connect the datasets. Furthermore, it would be really impractical to link all possible datasets to each other, as there are a huge number of them.

Instead, the approach is to find unique identifiers for cell values, and in particular...