Improving On-line Scientific Resource Profiling by Exploiting Resource Citation Information in the Literature

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.

Automated summary

This study focuses on utilizing citation information in scientific literature to extract relations between online resources and scientific terms, aiming to improve online scientific resource profiling. Experimental results show that our framework outperforms other methods by around 5% in scientific information extraction tasks, indicating a promising step towards utilizing citation information to enhance online scientific resource profiling.