Scientific Knowledge Graph-driven Research Profiling

Abstract

Due to the growing expansion of scientific literature and knowledge, access to scholarly contents is becoming more challenging. This paper presents a research profiling framework driven by scientific knowledge graphs (SKGs), which aims at achieving the deep fusion and thorough disclosure of scientific resources and domain knowledge, as well as satisfying the needs of researchers for knowledge overview and acquisition in a reasonable period of time using fine-grained and multi-dimensional profiling scenarios. Further, we conduct the experiment of the SKG construction task on a specific domain by combining scientific literature from Web Of Science and an open knowledge base, and develop a prototype system for user's single entity search to validate the approach, with profile presentations of overall graph view, literature profile, hotness topics list, high-impact experts, and providing a service of profile viewing and download.