In this extended abstract, we introduce a previously developed framework for the assessment of the quality of product reviews by using formal argumentation theory and machine learning.

Then, we outline current development on the use of knowledge graphs to better mine arguments, refine argumentation reasoning, and produce more meaningful explanations.

Improving Quality Assessment of Online Reviews UsingFormal Argumentation Theory and Knowledge Graphs

Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge.
However, such knowledge is not sufficient to understand the implicit reasoning link between individual argumentative components (i.e., claim and premise).
In this work, we focus on identifying the implicit knowledge in the form of argumentation knowledge which can help in understanding the reasoning link in arguments.
Being inspired by the Argument from Consequences scheme, we propose a semi-structured template to represent such argumentation knowledge that explicates the implicit reasoning in arguments via causality.
We create a novel two-phase annotation process with simplified guidelines and show how to collect and filter high quality implicit reasonings via crowdsourcing.
We find substantial inter-annotator agreement for quality evaluation between experts, but find evidence that casts a few questions on the feasibility of collecting high quality semi-structured implicit reasoning through our crowdsourcing process.
We release our materials (i.e., crowdsourcing guidelines and collected implicit reasonings) to facilitate further research towards the structured representation of argumentation knowledge.

Annotating Implicit Reasoning in Arguments with Causal Links

We propose a schema called ArguMeet for argument diagramming of multi-party meeting conversations. The argumentative portions are extracted from the meeting transcript and a visual representation is created which provides a high-level view of the argumentation that has taken place during the meeting resulting in a set of possible action points.

ArguMeet: An Argument Diagramming Schema for Meeting Conversations

First Workshop on Argumentation Knowledge Graphs 

Some of my past and current research has addressed the generation and mining of scientific argumentation in monological text in the domain of genetic counseling and genetics research articles.  Rather than representing argumentation in a knowledge graph (KG), arguments represented in propositional logic were generated from knowledge bases (KBs) using argumentation schemes.  Argumentation schemes are abstract patterns describing types of arguments that are acceptable to a community.  

My research relevant to the Argument Knowledge Graph workshop

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.

AKBC 2021 Workshop ArgKG Submissions

Improving Quality Assessment of Online Reviews UsingFormal Argumentation Theory and Knowledge Graphs

Annotating Implicit Reasoning in Arguments with Causal Links

ArguMeet: An Argument Diagramming Schema for Meeting Conversations

First Workshop on Argumentation Knowledge Graphs

My research relevant to the Argument Knowledge Graph workshop

End-to-end argumentation knowledge graph construction