@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance
%A Zhang, Kai
%A Li, Pengcheng
%A Song, Kaisong
%A Li, Xurui
%A Kang, Yangyang
%A Zhang, Xuhong
%A Liu, Xiaozhong
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhang-etal-2024-knowledge-triplets
%X 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.
%U https://aclanthology.org/2024.lrec-main.862
%P 9865-9877
Markdown (Informal)
[Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance](https://aclanthology.org/2024.lrec-main.862) (Zhang et al., LREC-COLING 2024)
ACL
- Kai Zhang, Pengcheng Li, Kaisong Song, Xurui Li, Yangyang Kang, Xuhong Zhang, and Xiaozhong Liu. 2024. Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9865–9877, Torino, Italia. ELRA and ICCL.