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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings.

Jie Wang, Zhanqiu Zhang, Zhihao Shi

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    Semantic matching models in knowledge graph embeddings (KGE) struggle with inner product limitations. A new method, DURA, uses dual models to ensure similar entities have similar embeddings, significantly improving KGE performance.

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    Area of Science:

    • Artificial Intelligence
    • Data Science
    • Machine Learning

    Background:

    • Semantic matching models are powerful for knowledge graph embeddings (KGE).
    • Existing models often use inner products to assess triple/quadruple plausibility.
    • Inner products can lead to dissimilar embeddings for semantically similar entities, limiting performance.

    Purpose of the Study:

    • To propose a novel regularizer, DUality-induced RegulArizer (DURA), to improve KGE.
    • To address the limitation of inner products in semantic matching KGE models.
    • To encourage semantically similar entities to have similar embeddings.

    Main Methods:

    • Introduced DURA, a novel regularizer for KGE.
    • Leveraged the duality between primal (semantic matching) and dual (distance-based) KGE models.
    • Applied DURA as constraints on entity embeddings.

    Main Results:

    • DURA consistently and significantly improves state-of-the-art semantic matching KGE models.
    • Performance enhancements were observed on both static and temporal knowledge graph benchmarks.
    • The proposed regularizer effectively encourages similar semantic embeddings.

    Conclusions:

    • DURA overcomes the limitations of inner product-based KGE models.
    • The duality-based approach offers a robust method for improving entity embeddings.
    • DURA represents a significant advancement in knowledge graph embedding techniques.