Adaptive Knowledge Graph Refinement for Oncology Insights Using Distant Learning
Kanchan Verandani
Lecture notes in networks and systems
Problems Identified (4)
Oncology Knowledge Structuring: Clinical decision-making in oncology requires structured domain knowledge extracted from large biomedical literature collections.
Manual Annotation Bottleneck: Oncological knowledge extraction needs approaches that avoid reliance on manual annotation.
Oncology Knowledge Structuring: Clinical decision-making in oncology requires structured domain knowledge extracted from large biomedical literature collections.
Manual Annotation Bottleneck: Oncological knowledge extraction needs approaches that avoid reliance on manual annotation.
Proposed Solutions (4)
Adaptive Distantly Supervised KG Refinement: The study proposes a framework for dynamically refining specialized knowledge graphs using distant supervision and iterative adaptation.
Domain-Adaptive Deep Biomedical IE: The system integrates domain adaptation with deep learning-based entity recognition and relationship extraction for oncological knowledge organization.
Adaptive Distantly Supervised KG Refinement: The study proposes a framework for dynamically refining specialized knowledge graphs using distant supervision and iterative adaptation.
Domain-Adaptive Deep Biomedical IE: The system integrates domain adaptation with deep learning-based entity recognition and relationship extraction for oncological knowledge organization.
Results (3)
Effective Domain-Specific Knowledge Discovery:
Scalable Automated Biomedical Discovery:
Effective Domain-Specific Knowledge Discovery:
Research Domain
Oncology biomedical knowledge graph refinement