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Transformers01:26

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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Updated: Aug 4, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Published on: June 13, 2025

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Position-Aware Relational Transformer for Knowledge Graph Embedding.

Guangyao Li, Zequn Sun, Wei Hu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 5, 2023
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    Summary
    This summary is machine-generated.

    Knowformer, a new Transformer model, improves knowledge graph (KG) embedding by using relational compositions. This approach captures entity roles and enhances KG representation learning for better performance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Natural Language Processing

    Background:

    • Transformer models excel in language and vision but are underutilized for knowledge graph (KG) embedding.
    • Standard self-attention (SA) in Transformers struggles with KG triples due to order invariance, failing to distinguish real from shuffled triples and capture correct semantics.
    • This limitation hinders the effective modeling of subject-relation-object relationships in KGs.

    Purpose of the Study:

    • To propose a novel Transformer architecture, Knowformer, for enhanced knowledge graph embedding.
    • To address the training inconsistency and semantic capture issues of standard Transformers in KG tasks.
    • To improve the ability to distinguish entity roles and capture relational semantics within KG triples.

    Main Methods:

    • Introduced Knowformer, a Transformer architecture incorporating relational compositions into entity representations.
    • Relational compositions, inspired by translational and semantic-matching techniques, explicitly inject semantics and define entity roles (subject/object).
    • A residual block integrates these compositions with SA, enabling layer-by-layer propagation of relational semantics.

    Main Results:

    • Knowformer effectively distinguishes entity roles based on their position in relation triples.
    • The model demonstrates the ability to correctly capture relational semantics, overcoming Transformer's order invariance limitations.
    • Extensive experiments on six benchmark datasets show state-of-the-art performance in link prediction and entity alignment.

    Conclusions:

    • Knowformer represents a significant advancement in KG embedding by leveraging Transformer architecture with relational compositions.
    • The proposed method successfully addresses the semantic capture challenges of standard Transformers for KG data.
    • Knowformer achieves superior performance, setting a new benchmark for KG embedding tasks like link prediction and entity alignment.