We study the task of on-line scientific resource profiling, which aims at better understanding and summarizing on-line scientific resources to promote resource search and recommendation systems. To this end we propose to exploit the resource citation information in scientific literature by extracting the fine-grained relations between the cited on-line resources and other resource-related scientific terms. In this paper we create a dataset (SciResTR) and develop a framework (SciResTR-IE) which jointly extracts all the related scientific terms and the resource-term relations. Extensive experiments demonstrate that our framework outperforms other baselines significantly, by around 5% in scientific information extraction tasks absolutely. We further show that our proposed system can automatically construct several on-line-resource-centered networks from a large corpus of scientific articles, which is a first step towards utilizing resource citation information in the literature to improve on-line scientific resource profiling.

我们研究在线科学资源概要分析的任务，该任务旨在更好地理解和总结在线科学资源，以促进资源搜索和推荐系统。为此，我们建议通过提取引用的在线资源与其他与资源相关的科学术语之间的细粒度关系来利用科学文献中的资源引用信息。在本文中，我们创建了一个数据集（SciResTR），并开发了一个框架（SciResTR-IE），该框架共同提取了所有相关的科学术语和资源术语关系。大量的实验表明，我们的框架明显优于其他基准，在科学信息提取任务中绝对超过了大约5％。我们进一步表明，我们提出的系统可以从大量的科学文章中自动构建几个以资源为中心的网络，这是利用文献中的资源引用信息来改善在线科学资源分析的第一步。