Effective synthesis of learning material is a multidimensional problem, which often relies on handpicking approaches and human expertise. Sources of educational content exist in a variety of forms, each offering proprietary metadata information and search facilities. This paper aims to show that it is possible to harvest scholarly resources from various repositories of open educational resources (OERs) in a federated manner. In addition, their subject can be automatically annotated using ontology inference and standard thematic terminologies.,Based on a semantic interpretation of their metadata, authors can align external collections and maintain them in a shared knowledge pool known as the Learning Object Ontology Repository (LOOR). The author leverages the LOOR and show that it is possible to search through various educational repositories’ metadata and amalgamate their semantics into a common learning object (LO) ontology. The author then proceeds with automatic subject classification of LOs using keyword expansion and referencing standard taxonomic vocabularies for thematic classification, expressed in SKOS.,The approach for automatic subject classification simply takes advantage of the implicit information in the searching and selection process and combines them with expert knowledge in the domain of reference (SKOS thesauri). This is shown to improve recall by a considerable factor, while precision remains unaffected.,To the best of the author’s knowledge, the idea of subject classification of LOs through the reuse of search query terms combined with SKOS-based matching and expansion has not been investigated before in a federated scholarly setting.

中文翻译：

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使用本体的语义注释和联合学术数据的收集

学习材料的有效综合是一个多方面的问题，通常依赖于精选方法和人类专业知识。教育内容的来源有多种形式，每种形式都提供专有的元数据信息和搜索工具。本文旨在表明有可能以联合方式从开放式教育资源（OER）的各个存储库中获取学术资源。此外，他们的主题可以使用本体推理和标准主题术语自动注释。基于元数据的语义解释，作者可以对齐外部集合并将其维护在一个称为“学习对象本体库”（LOOR）的共享知识库中。 。作者利用了LOOR，并表明可以搜索各种教育存储库的元数据并将其语义合并为一个通用的学习对象（LO）本体。然后，作者使用关键字扩展扩展LO的自动主题分类，并参考以SKOS表示的标准分类词汇进行主题分类。自动主题分类的方法只是在搜索和选择过程中利用隐式信息，并将其与参考领域的专家知识（SKOS thesauri）。事实证明，这在相当大的程度上提高了查全率，而精度却不受影响。据作者所知，