SGER: Relevance Models for Digital Repository Management

摘要IIS-0741326 Martin，James H.科罗拉多大学博尔德分校数字知识库管理的相关性模型这个SGER解决了与知识库开发和管理相关的问题。 要解决的一个关键问题是自动评估资源与主题领域的“相关性”，而无需人工领域专家的调解。 为了实现这一目标，该项目将研究、开发和评估用于构建相关性模型的机器学习方法。 研究人员以前的工作与模型“训练”，以自动判断资源的质量将被应用。 这些指标将与独立于领域的质量指标结合使用。 到目前为止探讨的一般质量指标包括有图表和解释性插图、良好的写作风格和结构以及权威性参考资料。 通过将其性能和结果与人类专家的性能和结果进行比较来评估模型。 当模型的性能接近专家的人类性能时，可以产生可以加速和简化数字存储库管理的工具。

AbstractIIS - 0741326Martin, James H.University of Colorado at BoulderRelevance Models for Digital Repository ManagementThis SGER addresses issues associated with repository development and management. A key issue to be addressed is automatic assessment of the "relevance" of a resource to a topical domain without the mediation of a human domain expert. To achieve this, the project will investigate, develop and evaluate machine learning methods for building relevance models. Previous work by the investigators with models "trained" to automatically judge quality of a resource will be applied. These will be used in conjunction with domain independent quality indicators. Among the general quality indicators explored to date are presence of graphics and explanatory illustrations, good writing style and structure, and authoritative references. The models are evaluated by comparing their performance and results with those of human experts. When performance of the models approximate expert human performance, tools can be produced that can expedite and simplify digital repository management.