Open Information Extraction for Knowledge Graph Construction

Abstract

An open information extraction approach for knowledge graph construction is presented. The motivation for the work is that large quantities of scholarly documents are available within many domains of discourse, and the subsequent challenge is to identify the most relevant articles concerning a particular topic. The proposed approach takes a document corpus and identifies triples within this corpus which are then processed to generate a literature knowledge graph. The extraction of triples is conducted using an open information extraction approach. The proposed OIE4KGC approach was evaluated using a bespoke clinical research methodology dataset and a benchmark dataset. A f-score of 51% was achieved on a clinical research methodology dataset and a f-score of 37% was achieved on the benchmark dataset.