From Research Articles to Knowledge Graphs
Vayianos Pertsas, Panos Constantopoulos · 2019
Understanding and extracting knowledge contained in text and encoding it as linked data for the WEB is a highly complex task that poses several challenges, requiring expertise from different fields such as conceptual modeling, natural language processing and web technologies including web mining, linked data generation and publishing, etc. When it comes to the scholarly domain, the transformation of human readable research articles into machine comprehensible knowledge bases is considered of high importance and necessity today due to the explosion of scientific publications in every major discipline, that makes it increasingly difficult for experts to maintain an overview of their domain or relate ideas from different domains. This situation could be significantly alleviated by knowledge bases capable of supporting queries such as: find all papers that address a given problem; how was the problem solved; which methods are employed by whom in addressing particular tasks; etc. that currently cannot be addressed by commonly used search engines such as Google Scholar or Semantic Scholar.