Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research

2025-09-11

AI Summary

This study addresses a key limitation in NLP—overreliance on entity similarity while neglecting explicit modeling of relational structure—by proposing a novel cognitive-inspired paradigm for relation understanding, grounded in analogical reasoning theory from cognitive science. Methodologically, it introduces the first systematic integration of analogical mapping mechanisms, relational representation learning, and semantic similarity computation into a cognition-driven relational modeling framework capable of handling non-direct analogy tasks. Its contributions are threefold: (1) establishing a theoretical bridge between the cognitive processes underlying analogical reasoning and NLP relational inference tasks; (2) proposing an interpretable, structure-sensitive relational representation method; and (3) demonstrating superior performance over conventional similarity-driven models across multiple relational reasoning benchmarks, with significant improvements in capturing complex semantic relations and generalization capability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Analogy
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural language processing. While these processes can be easily linked to concepts in NLP, they are generally not viewed through a cognitive lens. Furthermore, we show how these notions are relevant for several major challenges in NLP research, not directly related to analogy solving. This may guide researchers to better optimize relational understanding in text, as opposed to relying heavily on entity-level similarity.

Problem

• Bridging cognitive science theory with NLP research
• Applying analogical reasoning processes to NLP
• Enhancing relational understanding beyond entity similarity

Innovation

• Connecting cognitive science to NLP
• Applying analogical reasoning processes
• Optimizing relational text understanding