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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Brain-Inspired Search Engine Assistant Based on Knowledge Graph.

Xuejiao Zhao, Huanhuan Chen, Zhenchang Xing

    IEEE Transactions on Neural Networks and Learning Systems
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    This summary is machine-generated.

    DeveloperBot, a brain-inspired search assistant, answers complex developer queries with explainable reasoning. It uses a knowledge graph and cognitive models to provide accurate answers and confidence scores, enhancing trust.

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    Area of Science:

    • Artificial Intelligence
    • Cognitive Science
    • Information Retrieval

    Background:

    • Search engines struggle with complex queries, requiring extensive developer effort.
    • Existing question answering (Q&A) systems lack explainability, hindering user trust.
    • Developer needs for accurate, understandable, and trustworthy answers are unmet.

    Purpose of the Study:

    • Propose DeveloperBot, a novel brain-inspired search engine assistant.
    • Enable answering complex, multi-constraint queries with explainability.
    • Evaluate DeveloperBot's effectiveness in assisting developers' information needs.

    Main Methods:

    • Constructing a multilayer query graph from complex queries.
    • Modeling constraint reasoning as a subgraph search using spreading activation.
    • Extracting novel subgraph features for decision-making and explanation generation.

    Main Results:

    • DeveloperBot accurately estimates answers and their confidences.
    • Generated reasoning subgraphs and answer confidences provide explanations.
    • User study validates DeveloperBot's assistance for developer information needs.

    Conclusions:

    • DeveloperBot effectively addresses complex queries with explainable answers.
    • The brain-inspired approach enhances answer trustworthiness and user adoption.
    • This system offers a promising direction for intelligent search assistance.