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Updated: Oct 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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GCP: Graph Encoder With Content-Planning for Sentence Generation From Knowledge Bases.

Bayu Distiawan Trisedya, Jianzhong Qi, Wei Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 8, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a Graph encoder with Content-Planning capability (GCP) to convert knowledge graphs into natural sentences. GCP improves sentence generation by considering entity order, enhancing human understanding of complex data.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Knowledge Representation

    Background:

    • Knowledge graphs store facts as triples (subject-predicate-object), forming a graph structure.
    • Triple representation is machine-readable but not easily understood by humans.
    • Translating knowledge graphs into natural language is crucial for accessibility.

    Purpose of the Study:

    • To develop a system for translating knowledge graphs into human-readable natural language sentences.
    • To improve the naturalness and coherence of generated sentences from graph data.
    • To enhance the usability of knowledge bases for human users.

    Main Methods:

    • An encoder-decoder approach was employed for the translation task.
    • A novel Graph encoder with Content-Planning capability (GCP) was proposed.
    • GCP utilizes entity-order aware topological traversal for graph encoding and content planning.

    Main Results:

    • The proposed GCP system demonstrated improved performance over existing state-of-the-art models.
    • GCP achieved significant gains in BLEU, METEOR, and TER metrics by up to 3.6%, 4.1%, and 3.8%, respectively.
    • The system successfully generated sentences with proper entity mention ordering.

    Conclusions:

    • The GCP model effectively translates knowledge graphs into natural language sentences.
    • The content-planning capability of GCP enhances sentence generation quality and entity ordering.
    • This approach offers a promising solution for making knowledge graph information more accessible to humans.