Work

Bi-term association based on fuzzy logic

Year: 2026

Type: article

Abstract: 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.

Source: Array

DOI: 10.1016/j.array.2026.100704

Authors: Wensu Liu, Jing Wan, Na Lv, Xiaobei Zhou

Institution: China Medical University

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,680

Funders: Natural Science Foundation of Liaoning Province, Higher Education Discipline Innovation Project