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    This study introduces an incremental and distributed inference method for large-scale ontologies using MapReduce. The approach enhances reasoning performance and scalability for knowledge bases, especially when dealing with incremental data updates.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Semantic Web Technologies

    Background:

    • The rapid expansion of semantic data and ontology bases presents challenges for efficient and scalable reasoning.
    • Traditional centralized reasoning methods struggle with large-scale ontologies, necessitating distributed approaches.

    Purpose of the Study:

    • To propose an incremental and distributed inference method for processing large-scale ontologies.
    • To enhance the scalability and performance of reasoning, particularly for incremental knowledge bases.

    Main Methods:

    • Utilized MapReduce for distributed processing of large ontologies.
    • Developed a transfer inference forest and effective assertional triples to optimize storage and reasoning.
    • Implemented a prototype system on the Hadoop framework.

    Main Results:

    • Achieved high-performance reasoning and runtime searching capabilities.
    • Significantly reduced storage requirements.
    • Simplified and accelerated the reasoning process for incremental knowledge bases.

    Conclusions:

    • The proposed incremental and distributed inference method is usable and effective for large-scale ontologies.
    • The approach offers a scalable solution for semantic data reasoning in the face of increasing data volumes.