Project Details
Projekt
Between the Lines - Knowledge-enhanced Argument Analysis in a Formal Argumentation Reasoning System
Subject Area
Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
Term
from 2017 to 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 375654996
Argumentation is widely used in political discourse and general communication. Arguments vary in strength and quality, and what is explicitly said reflects only part of the knowledge and reasoning that underlies an argument, as understood by humans. This project will investigate methods to computationally analyze and validate arguments in order to i) complement the overtly expressed argument with automatically acquired knowledge that provides missing explanatory links, to ii) construct a formal, knowledge-enhanced analysis of the argument, and on this basis, iii) establish and verify the extended argument structure using a combination of machine learning and formal reasoning. We aim to advance current methods in argument analysis by developing a knowledge-enhanced formal argument reasoning system that analyses argumentative texts semantically. We achieve this by analyzing the semantic coherence between statements in an argument. We will link entities and concepts in the given statements to knowledge bases and learn to reconstruct implicitly understood background knowledge that enhances the argument's semantic coherence. As a result of this process we will obtain an abstract argumentation knowledge graph, constructed over explicit argumentative text and enriched with relevant implicit knowledge obtained from existing knowledge bases or harvested from textual sources. We apply supervised machine learning to detect abstract patterns in the linked knowledge, in order to determine and score relevant (types of) connecting knowledge. A formal reasoning process will then jointly establish the formal argument structure and determine the strength of the argument, based on the semantic coherence of each potential edge in the argumentation graph based on the type, the amount and the connectivity of the explicit statements and the added implicit knowledge.The outcome will be a semantically enriched formal representation of arguments linked to extensible knowledge sources: structured knowledge bases that are dynamically enriched from textual sources with various kinds of background and domain-specific information. We will employ linked knowledge repositories of factual knowledge (eg. DBpedia, or Gene Ontology), linguistic ontologies (WordNet), and large repositories of common sense knowledge (ConceptNet, OpenCyc) in conjunction with automatically harvested background knowledge, from relevant text or web corpora. We apply our methods to argumentation in general and special domains in different genres, and perform both component evaluations and benchmarking against existing systems.Within the SPP Robust Argumentation Machines our project focuses on Validation: knowledge-enhanced analysis will make the underlying logics and implicit assumptions of arguments explicit. The project will contribute to community actions, shared tasks and evaluation efforts by providing novel data sets on implied knowledge in arguments and an evaluation platform, among others.
DFG Programme
Priority Programmes
Subproject of
SPP 1999:
Robust Argumentation Machines (RATIO)
