Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA.
BMC Bioinformatics. 2010 Feb 24;11:103. doi: 10.1186/1471-2105-11-103.
In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles.
The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit.
The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.
在当前的科研时代，高效地传递信息至关重要。因此，学术和科学交流的性质正在发生变化；网络基础设施现在是绝对必要的，新媒体使信息和知识更具交互性和即时性。使知识更易于访问的一种方法是向学术文章添加机器可读的语义数据。
本文介绍的 Word 加载项可通过自动识别和突出显示可能包含丰富信息的单词或短语来帮助作者完成这项工作，从而使作者能够将语义数据与这些单词或短语相关联，并将这些数据作为 XML 嵌入文档中。该加载项及其源代码可在 http://www.codeplex.com/UCSDBioLit 上获得。
用于本体论术语识别的 Word 加载项使作者可以在撰写文档时向文档中添加语义数据，并使用 XML 标记对这些数据进行编码，这些标记在生命科学文献中实际上是一种标准。允许作者标记自己的作品将有助于增加机器可读文献元数据的数量和质量。
