End-to-End Argumentation Knowledge Graph Construction

verfasst von

Khalid Al-Khatib, Yufang Hou, Henning Wachsmuth, Charles Jochim, Francesca Bonin, Benno Stein

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.

Externe Organisation(en)

Bauhaus-Universität Weimar
IBM Research
Universität Paderborn

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

Proceedings of the AAAI Conference on Artificial Intelligence

Band

34

Seiten

7367-7374

Anzahl der Seiten

8

ISSN

2159-5399

Publikationsdatum

03.04.2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Artificial intelligence

Elektronische Version(en)

https://doi.org/10.1609/aaai.v34i05.6231 (Zugang: Offen)