From a single work, we can extract relationships from co-authors, citations, geo-location, dates, named entities, subject classification, institution affiliations, publishers and historical circulation information. Fromthese relationships, we can connect to other works, people, patents, events, _etc_. Creating, processing and making available this graph of new assertions at scale is big data. It requires the source data to be structured in such a way that many can consume and operate on the corpus in common ways. In 2008, _Nature_ published an entire issue on big data. Authors [PERSON] and [PERSON] [9] made the prescient statement:  
\"To search successfully for new science in large datasets, we must find unexpected patterns and interpret evidence in ways that frame new questions and suggest further explorations. Old habits of representing data can fail to meet these challenges, preventing us from reaching beyond the familiar questions and answers.\"  
This is precisely the issue being grappled with concerning library metadata. Though it is structured, it is not usable in a Web world of big data. This has led us to using linked data technologies as a primary enabler for interoperability at scale on the Web. In 2006, [PERSON] [10] recommended four rules--or \"expectations of behavior\"--that are useful in this discussion. Early innovation in exposing library data as linked data has shown there is a useful place in the larger web of big data. Some examples are shown in Table 1.  
In 2011, the British Library (BL) [11] announced the release of the British National Bibliography as linked data. Its purpose went beyond just encoding bibliographic data as RDF; rather, \"...they set out to model 'things of interest,' such as people, places and events, related to a book you might hold in your hand.\"  
The Deutsche National Bibliothek (DNB) [12] first released its authority data as linked data in 2010. Authority data are authorized name headings that are used by libraries to ensure the works of a single author are associated with each other. DNB followed this in 2012 when it released its bibliographic data, which describes specific titles and works, joining the BL in releasing the data that describes a major national library collection as open linked data. In May 2012, the US Library of Congress (LC) [13] announced it also would pursue releasing its bibliographic data as linked data. This was part of a larger project \"to help accelerate the launch of the Bibliographic Framework Initiative. A major focus of the project is to translate the MARC 21 format to a Linked Data (LD) model while retaining as much as possible the robust and beneficial aspects of the historical format.\" MARC (MAchine Readable Cataloging) [14] had been developed since the late 1960s to provide a method to encode bibliographic and authority data so it could be moved between systems; however, it was used only in the library community and is not appropriate for use in today's Semantic Web. OCLC's own efforts to expose linked data have spurred several innovative uses of existing data and increased traffic from search engines. Over this same time period OCLC has released several datasets \begin{table}
\begin{tabular}{l l} \hline
**Organization** & **Type of data** \\ \hline British Library & Bibliographic (item descriptions) \\ Deutsche National Bibliothek & Authority (names) and bibliographic \\ Library of Congress & Bibliographic \\ OCLC & Classification (Dewey Decimal), authority, bibliographic \\ \hline \end{tabular}
\end{table} 
Table 1: Examples of linked data. as linked data. Starting in late 2009, OCLC released Dewey classification data and has extended this data over the years to include deeper levels of the classification scheme [15]. This was followed in 2009 with the release of the Virtual International Authority File (VIAF) as linked data [16], and then the 2012 OCLC release of WorldCat as linked data using Schema.org as the primary vocabulary.