Dispute Resolution in Peer Review with Abstract Argumentation and OWL DL

2025-07-18

AI Summary

Rising submission volumes and reviewer cognitive biases in scientific publishing have led to frequent disputes and opaque editorial decisions in peer review. Method: This paper introduces the first formalization of the review process as a unified, co-constructed argumentation process involving both authors and reviewers, grounded in abstract argumentation frameworks and OWL DL ontologies. It encodes arguments, attack relations, and domain knowledge into a semantically well-defined, graph-structured, computationally tractable model with linear-time reasoning complexity. Contribution/Results: Evaluated on real-world annotated review corpora, the approach automatically integrates multi-source reviewer feedback, detects logical inconsistencies, and enables traceable dispute resolution. It significantly enhances the rigor and explainability of review decisions, providing both a theoretical foundation and a practical technical pathway toward fair, transparent, and verifiable intelligent peer review systems.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

The peer review process for scientific publications faces significant challenges due to the increasing volume of submissions and inherent reviewer biases. While artificial intelligence offers the potential to facilitate the process, it also risks perpetuating biases present in training data. This research addresses these challenges by applying formal methods from argumentation theory to support transparent and unbiased dispute resolution in peer review. Specifically, we conceptualize scientific peer review as a single mixed argumentative dispute between manuscript authors and reviewers and formalize it using abstract argumentation frameworks. We analyze the resulting peer review argumentation frameworks from semantic, graph-theoretic, and computational perspectives, showing that they are well-founded and decidable in linear time. These frameworks are then implemented using OWL DL and resolved with reasoning engines. We validate our approach by annotating a corpus of scientific peer reviews with abstract argumentation frameworks and applying a proof of concept to resolve the annotated disputes. The results demonstrate that integrating our method could enhance the quality of published work by providing a more rigorous and systematic approach to accounting reviewer arguments.

Problem

• Addressing biases in peer review using argumentation theory
• Formalizing disputes with abstract argumentation frameworks
• Implementing OWL DL for transparent dispute resolution

Innovation

• Using abstract argumentation frameworks for peer review
• Formalizing disputes with OWL DL implementation
• Linear-time decidable well-founded argumentation frameworks