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  <title>Repository Collection: null</title>
  <link rel="alternate" href="https://scholar.dgist.ac.kr/handle/20.500.11750/116" />
  <subtitle />
  <id>https://scholar.dgist.ac.kr/handle/20.500.11750/116</id>
  <updated>2026-04-05T16:40:42Z</updated>
  <dc:date>2026-04-05T16:40:42Z</dc:date>
  <entry>
    <title>A BERT-enhanced Graph Neural Network for Knowledge Base Population</title>
    <link rel="alternate" href="https://scholar.dgist.ac.kr/handle/20.500.11750/47777" />
    <author>
      <name>Lim, Heechul</name>
    </author>
    <author>
      <name>Kim, Min-Soo</name>
    </author>
    <id>https://scholar.dgist.ac.kr/handle/20.500.11750/47777</id>
    <updated>2025-07-25T03:29:40Z</updated>
    <published>2023-02-13T15:00:00Z</published>
    <summary type="text">Title: A BERT-enhanced Graph Neural Network for Knowledge Base Population
Author(s): Lim, Heechul; Kim, Min-Soo
Abstract: We present BGKBP, a deep-learning algorithm based on BERT, and a graph neural network for knowledge base population (KBP). Our experiments showed that a straightforward application of BERT and GNN on a large knowledge base (e.g., Wikidata) improves KBP quality and outperforms the previous state-of-the-art methods. We developed four techniques to improve the BGKBP&amp;apos;s KBP capability: (1) serialization, (2) fine-tuning, (3) node regression, and (4) text augmentation. BGKBP achieved the best F1 scores of 0.723 and 0.495 on entity linking and new entity detection, respectively. We further showed that using text augmentation (BGKBP-TA) achieved the best F1 score of 0.547 on relation linking, which is more difficult than entity linking because of the various representations of some of the relations. © 2023 IEEE.</summary>
    <dc:date>2023-02-13T15:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Toward Mission Critical A.I. Systems</title>
    <link rel="alternate" href="https://scholar.dgist.ac.kr/handle/20.500.11750/47123" />
    <author>
      <name>Kim, Min-Soo</name>
    </author>
    <id>https://scholar.dgist.ac.kr/handle/20.500.11750/47123</id>
    <updated>2025-07-25T04:10:50Z</updated>
    <published>2017-10-18T15:00:00Z</published>
    <summary type="text">Title: Toward Mission Critical A.I. Systems
Author(s): Kim, Min-Soo</summary>
    <dc:date>2017-10-18T15:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Big data technology for designing high-quality oligonucleotides without off-target effects</title>
    <link rel="alternate" href="https://scholar.dgist.ac.kr/handle/20.500.11750/47102" />
    <author>
      <name>Kim, Min-Soo</name>
    </author>
    <id>https://scholar.dgist.ac.kr/handle/20.500.11750/47102</id>
    <updated>2025-07-25T02:39:25Z</updated>
    <published>2017-11-01T15:00:00Z</published>
    <summary type="text">Title: Big data technology for designing high-quality oligonucleotides without off-target effects
Author(s): Kim, Min-Soo</summary>
    <dc:date>2017-11-01T15:00:00Z</dc:date>
  </entry>
  <entry>
    <title>A Distributed and Parallel Graph Processing and Generating Methods for Trillion-scale Graphs</title>
    <link rel="alternate" href="https://scholar.dgist.ac.kr/handle/20.500.11750/47039" />
    <author>
      <name>Kim, Min-Soo</name>
    </author>
    <id>https://scholar.dgist.ac.kr/handle/20.500.11750/47039</id>
    <updated>2025-07-25T03:37:30Z</updated>
    <published>2017-11-17T15:00:00Z</published>
    <summary type="text">Title: A Distributed and Parallel Graph Processing and Generating Methods for Trillion-scale Graphs
Author(s): Kim, Min-Soo</summary>
    <dc:date>2017-11-17T15:00:00Z</dc:date>
  </entry>
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