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. 

The EXPLAIN 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. We will apply supervised machine learning to detect abstract patterns in the linked knowledge, in order to determine and score relevant (types of) connecting knowledge, 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. 

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.

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. 

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.