## 2 Case Studies
### Migrating Annotations across Multiple Representations of Museum Artefacts
Figure 8 illustrates an instance of the OA data model that represents an automatically extracted tag (\"running\") that has been assigned to a specific temporal segment (from 120 s to 125 s) of accelerometry data and associated video, using an _oa:FragmentSelector_ containing a W3C Media Fragment (\"t = 120,125\"). Figure 7: High level architectural view of the SAAR system. ## 3 Implementation

Each of the case studies described above makes use of custom annotation client software, which has been developed to provide a user interface tailored to the Annotation types and user needs, specific to the community. However, by adopting the common OA model within these applications, we have been able to develop a generic Open Source Annotation repository, lorestore [22], which can be used to store, search and retrieve Annotations for all of the case studies described above. The lorestore repository was developed as a Java Web application on top of the Sesame 2.0 RDF framework, and uses a Named Graph to store each Annotation and ORE-compliant data body. A REST API is provided to support CRUD operations on Annotations and bodies (Create, Read, Update, Delete) as well as search and retrieval by target URI or via keyword search. A SPARQL endpoint has also been implemented to enable custom queries, and ATOM feeds are implemented to allow subscription to, or harvesting of Annotations by target. The repository supports HTTP content negotiation so that Annotations can be retrieved by their identifying URI in a variety of serialization formats including TriG, TriX, RDF/XML and JSON. Authentication and user account management are implemented using the Emmet framework over Spring security, which supports basic role-based, OpenID and/or Shibboleth-based authentication. Annotations can be flagged as private, so that only the owner can view or modify their content, or locked, so that they cannot be modified. A Web-based User Interface for the repository provides a search interface and renders Annotations from their underlying RDF representation to human-readable HTML as well as diagrammatic representations. For administrators, the Web interface also supports various content management and user management operations. Figure 9 illustrates the technical components of our generic Annotation repository, lorestore. Figure 8: OA representation of an automatically generated animal activity tag. In the near future we will begin implementation of a Web service that validates an annotation by checking its conformance with the Open Annotation Core ontology. This service will return a positive or negative message, with regard to compliance, prior to the annotation being saved in the backend repository. The validation Web service will be designed so that it can interact with any authoring tool or backend repository. It is shown in Figure 9 between the Client and the Security Layer. ## 4 Discussion and Evaluation

The exercise described above--evaluating the OA data model through a number of domain-specific use cases - has enabled us to identify the model's strengths and weaknesses. The main strengths of the OA model that we have identified are as follows:

* _The OA model supports multiple targets, and each can be associated with a selector for specifying the segment of interest._ This has allowed us to create annotations that describe textual variation across multiple textual documents, to migrate a single annotation across multiple representations of a 3D museum object, and to attach a semantic tag to both accelerometry data streams and video that documents an animal's movements--without extending the model or creating aggregate targets. By comparison, previous annotation data models, such as the Annotea ontology that underpins AO were designed with the assumption of a single target, so they provide no mechanism for associating an Annotea _Context_ (used for describing the segment of a target resource) with a specific target.