End-to-End Argumentation Knowledge Graph Construction

  • Khalid Al-Khatib Bauhaus-Universität Weimar
  • Yufang Hou IBM Research
  • Henning Wachsmuth Paderborn University
  • Charles Jochim IBM Research
  • Francesca Bonin IBM Research
  • Benno Stein Bauhaus-Universität Weimar

Abstract

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

Published
2020-04-03
Section
AAAI Technical Track: Natural Language Processing