Bi-term association based on fuzzy logic

Traditional Boolean search strategies identify co-occurrence but fail to capture semantic relationships. Current relation extraction (RE) frameworks, which rely on domain-specific training, struggle with adaptability to emerging research topics. To address these limitations, we propose a novel framework integrating fuzzy logic with zero-shot large language models (LLMs), enabling the quantification of bi-term associations without explicit training data. Bi-term associations are defined as contextually grounded relationships that extend co-occurrence analysis while avoiding the data annotation requirements of traditional RE. Key contributions include: a fuzzy logic framework for graded membership assignment to capture uncertainty and relationship strength, design of complex fuzzy rules within a lightweight zero-shot LLM, and detailed demonstration of four conceptual case studies in diverse fields, ranging from simple to complex rule design. Benchmark comparisons on RE tasks show that the fuzzy logic method outperforms the baseline model (Qwen3:30b-a3b): F1 scores improve by 0.05 (n2c2 dataset: 0.82 vs. 0.77) and 0.17 (GAD dataset: 0.52 vs. 0.35). However, the fuzzy method is computationally intensive. This work introduces a bi-term association approach based on fuzzy logic to bridge co-occurrence analysis and RE, demonstrating its potential for biomedical knowledge discovery while addressing critical computational challenges in real-world deployment.