## 3 Solution in Knowledge Graph and Linked Data  
In order to manage authority of text strings, we must first think about the organization of top-level entities relevant to our industry needs. This organization is often referred to as an \"upper ontology\" or, more recently, as entities in a knowledge graph. A knowledge graph is a model of relationships between entities or objects in a given space. Given libraries' broad responsibility in organizing knowledge, the entities in a library knowledge graph must be quite broad and include the following key entities:  
* **People:** Traditionally people who have formally published works, but increasingly we must account for all people whether they are writing, reviewing, publishing or simply using library content;
* **Items:** Physical items held within libraries such as books and media, but also electronic-only information such as e-books, journal articles and digital scans of real objects;
* **Places:** Geographic locations past and present must be maintained to understand context of published works. Political names are important, but so are more abstract names such as Northern Gulf States;
* **Events:** Events can account for grand events such as a public performance by a rock music group, and also minor events such as a user viewing a book text;
* **Organizations:** Organization entities encompass corporate names, publishers, political parties and bodies, and associations;
* **Concepts:** Subject classification systems such as the Dewey Decimal Classification system and Library of Congress Class system are well-known concept organization schemes. More free-form tagging systems also round out this category. The models produced are being defined in RDF schemas. These schemas or vocabularies represent the most tangible output of the work in the knowledge organization space. Two recent efforts are attracting the most attention. The first is the Schema.org effort led by Google Google (2006) and the library extension to this vocabulary initiated by OCLC and evaluated within the W3C Community Groups (2006). Some parts of these extensions are being integrated back into the core schema. The second effort is the Bibliographic Framework Transition Initiative launched by the Library of Congress (2006). The former focuses on simplicity and interoperability, including systems outside of the direct sphere of the library. The latter focuses on the transition from MARC21 to modern standards necessary to preserve the precision and detail required for library workflows that include cataloging, resource sharing, preservation and inventory management. The selection of a schema, framework or vocabulary can be daunting, technically complex and fraught with emotion and politics within a given community. We have adopted the strategy of selecting broadly used models and developing accessible systems to achieve the most interoperability. It is not our strategy to select and prove a single schema is the best for all. We have selected the Schema.org vocabulary as the root of our data model as it provides an accessible core. Schema.org was launched as a collaboration among Google, Bing and Yahoo! to create a common set of schemas to encourage structured data markup within Web pages. While initially viewed as overly simplistic, the organization has been very collaborative with established communities of practice to extend and generally improve the quality of their schemas. OCLC was an early collaborator focusing on items typically held by libraries. We worked with staff within Google to understand the possibilities and determine the best places to contribute. Some of our initial extensions were to recognize the differences between content and the carrier in published works. For example, when requesting or offering a movie within libraries, or anywhere on the Web for that matter, it is important to distinguish whether it is a digital download, a DVD, a CD or VHS. This is data well-structured and used in libraries but not immediately necessary for a movie review site.