A Toolbox for the Nearly-Unsupervised Construction of Digital Library Knowledge Graphs

Abstract

Knowledge graphs are essential for digital libraries to store entity-centric knowledge. The applications of knowledge graphs range from summarizing entity information over answering complex queries to inferring new knowledge. Yet, building knowledge graphs means either relying on manual curation or designing supervised extraction processes to harvest knowledge from unstructured text. Obviously, both approaches are cost-intensive. Yet, the question is whether we can minimize the efforts to build a knowledge graph. And indeed, we propose a toolbox that provides methods to extract knowledge from arbitrary text. Our toolkit bypasses the need for supervision nearly completely and includes a novel algorithm to close the missing gaps. As a practical demonstration, we analyze our toolbox on established biomedical benchmarks. As far as we know, we are the first who propose, analyze and share a nearly unsupervised and complete toolbox for building knowledge graphs from text.