Collaborative Semantic Management and Automated Analysis of Scientific Literature

摘要

The overabundance of literature available in online repositories is an ongoing challenge for scientists that have to efficiently manage and analyze content for their information needs. Most of the existing literature management systems merely provide support for storing bibliographical metadata, tagging, and simple annotation capabilities. In this demo paper, we go beyond these approaches by demonstrating how an innovative combination of semantic web technologies with natural language processing can mitigate the information overload by helping in curating and organizing scientific literature. We present the Zeeva system as a first prototype that demonstrates how we can turn existing papers into a queryable knowledge base.