by Rachel Wang and Jordan Pedersen

Our entire world wide web already is composed of linked data. It is not surprising that cultural instutions which provide and curate open data, such as our libraries, archives and museums have begun to transform their metadata into linked data. In this talk we will explain why linked data is powerful and demonstrate the process of how to extract data insights from it using the python modules RDFlib and plotly. RDFlib is a powerful library used for working with triple data and representing information. As we will learn in this talk, linked data is queried with a query language called SPARQL which is supported by the RDFlib library. We’ll move from parsing data and then bring out your inner artist with plotly to create visualizations. The plot will thicken when we briefly touch upon how machine learning can be applied to linked data and the ways in which working with linked metadata is different and has unique promises not present in other forms of linked data. By the end of this talk you will be able to see for yourself how to draw relationships out of open linked data and the vale of communicating the relationships visible in linked data.

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