Probabilistic Reasoning in Commonsense Knowledge Bases for Natural Language Understanding: A Bayesian Network Perspective

Abstract

Human-like natural language understanding (NLU) requires machines to in- terpret implicit meaning through commonsense reasoning—a task complicated by the contextual variability and uncertainty inherent in real-world communication. This paper presents a Bayesian network framework for integrating probabilistic reasoning with struc- tured commonsense knowledge bases, addressing the challenge of dynamically modeling dependencies among abstract concepts during semantic parsing. We formalize common- sense knowledge triples as nodes within a directed acyclic graph, where edge weights encode conditional probabilities derived from both corpus statistics and ontological con- straints. A hybrid parameter estimation technique combines maximum likelihood esti- mation with entropy regularization to balance empirical data fidelity against ontological consistency. The network’s inferential capacity is demonstrated through three case stud- ies: metaphor interpretation, pragmatic implicature resolution, and multi-hop reason- ing under uncertainty. Quantitative evaluation against the ConceptNet and GenericsKB benchmarks reveals a 14.7% improvement in reasoning accuracy over rule-based base- lines, with particular gains in handling negations (23.1% error reduction) and speculative statements. The model’s ability to perform exact inference via junction tree algorithms while maintaining O(n log n) complexity for sparsely connected graphs makes it compu- tationally tractable for real-time NLU applications. These results suggest that Bayesian formalisms provide a mathematically rigorous substrate for operationalizing commonsense reasoning, offering advantages in scalability, interpretability, and uncertainty quantifica- tion compared to purely neural approaches.