@inproceedings{zhang-etal-2024-knowledge-triplets,
    title = "Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance",
    author = "Zhang, Kai  and
      Li, Pengcheng  and
      Song, Kaisong  and
      Li, Xurui  and
      Kang, Yangyang  and
      Zhang, Xuhong  and
      Liu, Xiaozhong",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.862",
    pages = "9865--9877",
    abstract = "Scientific Information Extraction (SciIE) is a vital task and is increasingly being adopted in biomedical data mining to conceptualize and epitomize knowledge triplets from the scientific literature. Existing relation extraction methods aim to extract explicit triplet knowledge from documents, however, they can hardly perceive unobserved factual relations. Recent generative methods have more flexibility, but their generated relations will encounter trustworthiness problems. In this paper, we first propose a novel Extraction-Contextualization-Derivation (ECD) strategy to generate a document-specific and entity-expanded dynamic graph from a shared static knowledge graph. Then, we propose a novel Dual-Graph Resonance Network (DGRN) which can generate richer explicit and implicit relations under the guidance of static and dynamic knowledge topologies. Experiments conducted on a public PubMed corpus validate the superiority of our method against several state-of-the-art baselines.",
}

Scientific Information Extraction (SciIE) is a vital task and is increasingly being adopted in biomedical data mining to conceptualize and epitomize knowledge triplets from the scientific literature. Existing relation extraction methods aim to extract explicit triplet knowledge from documents, however, they can hardly perceive unobserved factual relations. Recent generative methods have more flexibility, but their generated relations will encounter trustworthiness problems. In this paper, we first propose a novel Extraction-Contextualization-Derivation (ECD) strategy to generate a document-specific and entity-expanded dynamic graph from a shared static knowledge graph. Then, we propose a novel Dual-Graph Resonance Network (DGRN) which can generate richer explicit and implicit relations under the guidance of static and dynamic knowledge topologies. Experiments conducted on a public PubMed corpus validate the superiority of our method against several state-of-the-art baselines.
Zhang, Kai Li, Pengcheng Song, Kaisong Li, Xurui Kang, Yangyang Zhang, Xuhong Liu, Xiaozhong
Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance