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Scaling Biomedical Knowledge Graph Retrieval for Interpretable Reasoning: Applications to Clinical Diagnosis

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Summary
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LogosKG offers efficient multi-hop retrieval over large biomedical knowledge graphs (KGs). This hardware-optimized system improves diagnostic reasoning by enabling interpretable, deep graph traversal.

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Area of Science:

  • Biomedical Informatics
  • Graph Databases
  • Computational Biology

Background:

  • Biomedical knowledge graphs (KGs) are crucial for organizing complex biological and clinical data.
  • Scalable and interpretable multi-hop retrieval from large KGs is a significant computational challenge.
  • Current systems struggle with the scale and depth required for advanced biomedical reasoning.

Purpose of the Study:

  • To develop a hardware-optimized system, LogosKG, for efficient k-hop retrieval over large biomedical KGs.
  • To address the computational bottleneck in accessing deep, multi-hop graph structures.
  • To enhance diagnostic reasoning through improved KG traversal.

Main Methods:

  • LogosKG utilizes symbolic graph formulations and hardware-efficient execution for k-hop traversal.
  • Key techniques include degree-aware partitioning, cross-partition routing, and on-demand caching.
  • The system is designed to scale to billion-edge graphs while maintaining retrieval accuracy.

Main Results:

  • LogosKG demonstrates substantial efficiency gains compared to CPU- and GPU-based methods.
  • The system successfully scales to very large biomedical graphs (billion-edge scale).
  • Experiments show improved retrieval fidelity and performance.

Conclusions:

  • LogosKG provides a scalable and efficient solution for multi-hop retrieval in deep biomedical KGs.
  • Access to deep graph structures via LogosKG facilitates interpretable diagnostic evidence propagation.
  • Evaluation indicates enhanced diagnostic reasoning quality (accuracy, comprehensibility, succinctness) using high-hop KG retrieval.