Many Web portals allow users to associate additional information with existing multimedia resources such as images, audio, and video. However, these portals are usually closed systems and user-generated annotations are almost always kept locked up and remain inaccessible to the Web of Data. We believe that an important step to take is the integration of multimedia annotations and the Linked Data principles. We present the current state of the Open Annotation Model, explain our design rationale, and describe how the model can represent user annotations on multimedia Web resources. Applying this model in Web portals and devices, which support user annotations, should allow clients to easily publish and consume, thus exchange annotations on multimedia Web resources via common Web standards.
The different forms and functions that annotations can take are analyzed by Marshall @cite_5 . She distinguishes between and annotations, whereby formal annotations follow structural standards and informal ones are unstructured. Furthermore, Marshall divides into annotations that are intended for sharing and annotations of personal nature, often interpretable only by the original creator. Further divisions defined by Marshall with regard to the function of an annotation include vs. , vs. , vs. , vs. and vs. vs. . The difference between personal and public annotations in a digital environment is further investigated in a study by Marshall and Brush @cite_23 . They derive design implications for annotation systems, e.g. regarding find and filtering requirements, and user interface strategies for processing and sharing annotations.
@cite_5 @cite_23
2136409007 2124489423 2018830110 2485771844
Today people typically read and annotate printed documents even if they are obtained from electronic sources like digital libraries If there is a reason for them to share these personal annotations online, they must re-enter them. Given the advent of better computer support for reading and annotation, including tablet interfaces, will people ever share their personal digital ink annotations as is, or will they make substantial changes to them? What can we do to anticipate and support the transition from personal to public annotations? To investigate these questions, we performed a study to characterize and compare students' personal annotations as they read assigned papers with those they shared with each other using an online system. By analyzing over 1, 700 annotations, we confirmed three hypotheses: (1) only a small fraction of annotations made while reading are directly related to those shared in discussion; (2) some types of annotations - those that consist of anchors in the text coupled with margin notes - are more apt to be the basis of public commentary than other types of annotations; and (3) personal annotations undergo dramatic changes when they are shared in discussion, both in content and in how they are anchored to the source document. We then use these findings to explore ways to support the transition from personal to public annotations. As news reading becomes more social, how do different types of annotations affect people's selection of news articles? This paper reports on results from two experiments looking at social annotations in two different news reading contexts. The first experiment simulates a logged-out experience with annotations from strangers, a computer agent, and a branded company. Results indicate that, perhaps unsurprisingly, annotations by strangers have no persuasive effects. However, surprisingly, unknown branded companies still had a persuasive effect. The second experiment simulates a logged-in experience with annotations from friends, finding that friend annotations are both persuasive and improve user satisfaction over their article selections. In post-experiment interviews, we found that this increased satisfaction is due partly because of the context that annotations add. That is, friend annotations both help people decide what to read, and provide social context that improves engagement. Interviews also suggest subtle expertise effects. We discuss implications for design of social annotation systems and suggestions for future research. Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework. Unlike unstructured documents which only consist of plain text, semi-structured documents contain plenty of structured information including metadata, document fields, annotations, etc. Content-based filtering is to identify user-interested documents from a stream of documents based on the analysis of document content. When dealing with semi-structured documents, many existing filtering approaches either ignore the structured information, or simply use them as features. This dissertation focuses on the better use of document structured information for content-based filtering. We find that structured information is useful in the following problems. First, structured information is useful for user profile initialization in topic tracking tasks. At the early stage of a topic tracking task, the system performance tends to be low due to the limited number of labeled documents from the user. To deal with this problem, we propose two new user feedback mechanisms based on document structured information (facet-value pairs and Wikipedia concepts respectively). The new feedback mechanisms allow the system to quickly get some feedback from the user and refine the user profile. Our experiment results show that the new user feedback can significantly improve the filtering performance in topic tracking tasks. Second, structured information is useful for semi-structured document summarization in retrieval filtering tasks with user quries. In a retrieval filtering task where many documents are delivered, the user selects documents to read based on the short summaries of documents. In this sense, document summaries should be informative enough so that the user can make right decisions on which documents to read. To achieve this goal, we propose a new document-summarization method that can generate better summaries for semi-structured documents with rich metadata in filtering retrieval scenarios. Third, structured information can be easily incorporated into discriminative models for personalized recommendation. We propose two flexible Bayesian hierarchical models for joint user profile learning. The proposed models are discriminative, thus can easily incorporate various types of document structured information. They also have the advantages of being able to borrow discriminative information from similar users and capture multiple interests of individual users.