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T06. RAG, knowledge graphs, ontologies, and provenance

Retrieval-augmented generation and structured knowledge with traceable sources.

  1. T06-14Similar problemSince 2025

    Enhancing retrieval-augmented generation for interoperable industrial knowledge representation and inference toward cognitive digital twins

    Authors
    Dachuan Shi, Jianzhang Li, Olga Meyer, Thomas Bauernhansl
    Year
    2025
    Venue
    Computers in Industry vol. 171, 2025, 104330

    Korean review of T06-14

  2. T06-2Similar problem

    Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Authors
    Patrick Lewis and 11 others
    Year
    2020
    Venue
    NeurIPS 2020

    Korean review of T06-2

  3. T06-4Similar problem

    Retrieval-Augmented Generation for Large Language Models: A Survey

    Authors
    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang
    Year
    2024
    Venue
    arXiv (v1 2023-12-18, v5 2024-03-27)

    Korean review of T06-4

  4. T06-9Similar problem

    Measuring Attribution in Natural Language Generation Models

    Authors
    Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Lora Aroyo, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, David Reitter
    Year
    2023
    Venue
    Computational Linguistics vol. 49 no. 4, 2023, pp. 777-840

    Korean review of T06-9

  5. T06-12Similar problem

    Knowledge Graphs

    Authors
    Aidan Hogan and 17 others
    Year
    2022
    Venue
    ACM Computing Surveys vol. 54 no. 4, 71, pp. 1-37

    Korean review of T06-12

  6. T06-13Similar problem

    A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

    Authors
    Muhammad Yahya, Aabid Ali, Qaiser Mehmood, Lan Yang, John G. Breslin, Muhammad Intizar Ali
    Year
    2024
    Venue
    Semantic Web vol. 15 no. 2, 2024, pp. 461-479

    Korean review of T06-13

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