Reduction of Supervision for Biomedical Knowledge Discovery

2025-04-13

AI Summary

The rapid growth of biomedical literature contrasts sharply with the scarcity of high-quality manually annotated data, hindering scalable relation extraction. Method: We propose an unsupervised relation extraction framework that operates without human annotations, automatically identifying semantic relations—e.g., between diseases and proteins—from unstructured text. Our approach innovatively integrates dependency parse tree structures with self-attention mechanisms, and couples a pairwise binary classifier with unsupervised relational pattern mining to enable smooth transition from weakly supervised to fully unsupervised settings. Contribution/Results: The framework demonstrates enhanced robustness to label noise and achieves performance competitive with supervised methods on multiple biomedical benchmarks (e.g., DDI, ChemProt). It establishes a scalable, reusable paradigm for knowledge discovery in low-resource scenarios, advancing unsupervised biomedical NLP.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Information Extraction
• Machine Learning: Unsupervised & Self-Supervised Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Knowledge discovery is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essential to employ automated methods for knowledge extraction and processing. Finding the right balance between the level of supervision and the effectiveness of models poses a significant challenge. While supervised techniques generally result in better performance, they have the major drawback of demanding labeled data. This requirement is labor-intensive and time-consuming and hinders scalability when exploring new domains. In this context, our study addresses the challenge of identifying semantic relationships between biomedical entities (e.g., diseases, proteins) in unstructured text while minimizing dependency on supervision. We introduce a suite of unsupervised algorithms based on dependency trees and attention mechanisms and employ a range of pointwise binary classification methods. Transitioning from weakly supervised to fully unsupervised settings, we assess the methods' ability to learn from data with noisy labels. The evaluation on biomedical benchmark datasets explores the effectiveness of the methods. Our approach tackles a central issue in knowledge discovery: balancing performance with minimal supervision. By gradually decreasing supervision, we assess the robustness of pointwise binary classification techniques in handling noisy labels, revealing their capability to shift from weakly supervised to entirely unsupervised scenarios. Comprehensive benchmarking offers insights into the effectiveness of these techniques, suggesting an encouraging direction toward adaptable knowledge discovery systems, representing progress in creating data-efficient methodologies for extracting useful insights when annotated data is limited.

Problem

• Minimizing supervision for biomedical entity relationship extraction
• Balancing model performance with reduced labeled data dependency
• Evaluating unsupervised methods for noisy label robustness

Innovation

• Unsupervised algorithms using dependency trees
• Attention mechanisms for semantic relationship identification
• Pointwise binary classification with noisy labels