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    This study introduces a new multimodal knowledge graph completion (MMKGC) framework that effectively uses both similarities and differences across data types. The CISEDE method achieves state-of-the-art results in knowledge graph enhancement.

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    Area of Science:

    • Artificial Intelligence
    • Data Science
    • Computer Science

    Background:

    • Multimodal knowledge graph completion (MMKGC) integrates diverse data for enhanced knowledge graphs.
    • Existing methods often overlook complementary features or similarities between modalities.
    • Effectively associating heterogeneous modalities remains a significant challenge in MMKGC.

    Purpose of the Study:

    • To propose a novel MMKGC framework that leverages both similarities and differences among multimodal entities.
    • To introduce a cross-modal interaction mechanism designed for effective MMKGC.
    • To improve the precision and breadth of knowledge graph applications through multimodal data integration.

    Main Methods:

    • Developed a Cross-modal Interaction with Similarity-Enhancing and Difference-Embracing (CISEDE) framework.
    • Employed a cross-modal interaction mechanism utilizing multihead attention.
    • Integrated relation-guided fusion to decode and merge modal triples for MMKGC.

    Main Results:

    • The CISEDE framework demonstrated state-of-the-art performance on benchmark datasets (FB15k-237, WN9, WN18RR).
    • The proposed method effectively leverages both shared and unique information across different data modalities.
    • Achieved significant improvements in knowledge graph completion accuracy.

    Conclusions:

    • The CISEDE framework offers a powerful approach to MMKGC by embracing multimodal data heterogeneity.
    • The cross-modal interaction mechanism is key to enhancing MMKGC performance.
    • This work advances the field by providing a more comprehensive method for multimodal knowledge graph completion.