The Experts below are selected from a list of 11841 Experts worldwide ranked by ideXlab platform

Marc Davis - One of the best experts on this subject based on the ideXlab platform.

  • mobile media Metadata Metadata Creation system for mobile images
    ACM Multimedia, 2004
    Co-Authors: Marc Davis
    Abstract:

    In the 2003, more camera phones were sold worldwide than digital cameras. With this new platform, we can leverage regularities in the spatio-temporal context and social community of media capture and use to infer media content. We created and deployed a "Mobile Media Metadata" (MMM) prototype on Nokia 3650 camera phones with 55 users that uses "context-to-content" inferencing, a shared Metadata ontology, and user interaction at the point of capture to effectively infer media content annotations, specifically the semantic description of the location of the subject of users' photos.

  • Metadata Creation system for mobile images
    International Conference on Mobile Systems Applications and Services, 2004
    Co-Authors: Risto Sarvas, Erick Herrarte, Anita Wilhelm, Marc Davis
    Abstract:

    The amount of personal digital media is increasing, and managing it has become a pressing problem. Effective management of media content is not possible without content-related Metadata. In this paper we describe a content Metadata Creation process for images taken with a mobile phone. The design goals were to automate the Creation of image content Metadata by leveraging automatically available contextual Metadata on the mobile phone, to use similarity processing algorithms for reusing shared Metadata and images on a remote server, and to interact with the mobile phone user during image capture to confirm and augment the system supplied Metadata. We built a prototype system to evaluate the designed Metadata Creation process. The main findings were that the Creation process could be implemented with current technology and it facilitated the Creation of semantic Metadata at the time of image capture.

Ben Ryan - One of the best experts on this subject based on the ideXlab platform.

  • quality assurance for digital learning object repositories issues for the Metadata Creation process
    Research in Learning Technology, 2004
    Co-Authors: Sarah Currier, J Barton, Ronan Obeirne, Ben Ryan
    Abstract:

    Metadata enables users to find the resources they require, therefore it is an important component of any digital learning object repository. Much work has already been done within the learning technology community to assure Metadata quality, focused on the development of Metadata standards, specifications and vocabularies and their implementation within repositories. The Metadata Creation process has thus far been largely overlooked. There has been an assumption that Metadata Creation will be straightforward and that where machines cannot generate Metadata effectively, authors of learning materials will be the most appropriate Metadata creators. However, repositories are reporting difficulties in obtaining good quality Metadata from their contributors, and it is becoming apparent that the issue of Metadata Creation warrants attention. This paper surveys the growing body of evidence, including three UK-based case studies, scopes the issues surrounding human-generated Metadata Creation and identifies questions for further investigation. Collaborative Creation of Metadata by resource authors and Metadata specialists, and the design of tools and processes, are emerging as key areas for deeper research. Research is also needed into how end users will search learning object repositories. DOI: 10.1080/0968776042000211494

Han Jia - One of the best experts on this subject based on the ideXlab platform.

  • scientific referential Metadata Creation with information retrieval and labeled topic modeling
    2013
    Co-Authors: Han Jia, Xiaozhong Liu
    Abstract:

    The goal of this research is to propose an innovative method of creating scientific referential Metadata for a cyberinfrastructure-enabled learning environment to enhance learning experiences and to help students and scholars obtain better understanding of scientific publications. By using information retrieval, topic modeling, and meta-search approaches, different types of resources, such as related Wikipedia Pages, Datasets, Source Code, Video Lectures, Presentation Slides, and (online) Tutorials, for an assortment of publications and scientific (labeled) topics will be automatically retrieved, associated, and ranked. In order to test our method of automatic cyberlearning referential Metadata generation, we designed a user experiment for the quality of the Metadata for each scientific keyword and publication and resource ranking algorithms. Evaluation results based on MAP, MRR, and NDCG show that the cyberlearning referential Metadata retrieved via meta-search and statistical relevance ranking can effectively help students better understand the essence of scientific keywords and publications.

  • scientific cyberlearning resources referential Metadata Creation via information retrieval
    ACM IEEE Joint Conference on Digital Libraries, 2012
    Co-Authors: Xiaozhong Liu, Han Jia
    Abstract:

    The goal of this research is to describe an innovative method of creating scientific referential Metadata for a cyberinfrastructure-enabled learning environment to enhance student and scholar learning experiences. By using information retrieval and meta-search approaches, different types of referential Metadata, such as related Wikipedia Pages, Datasets, Source Code, Video Lectures, Presentation Slides, and (online) Tutorials, for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked.

Rachel Jaffe - One of the best experts on this subject based on the ideXlab platform.

Rudi Studer - One of the best experts on this subject based on the ideXlab platform.

  • leveraging Metadata Creation for the semantic web with cream
    Lecture Notes in Computer Science, 2003
    Co-Authors: Siegfried Handschuh, Steffen Staab, Rudi Studer
    Abstract:

    The success of the Semantic Web crucially depends on the easy Creation of ontology-based Metadata by semantic annotation. We provide a framework, CREAM, that allows for the Creation of semantic Metadata about static and dynamic Web pages, i.e. for semantic annotation of the Shallow and the Deep Web. CREAM supports the manual and the semi-automatic annotation of static Web pages, the authoring of new web pages with the simultaneous Creation of Metadata, and the deep annotation of Web pages defined dynamically by database queries.