ArticleScientific data2026
A Chinese Named Entity Recognition Dataset for Intangible Cultural Heritage.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A Chinese Named Entity Recognition Dataset for Intangible Cultural Heritage.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Named entity recognition (NER) is essential for structuring knowledge in the field of intangible cultural heritage (ICH), supporting applications such as knowledge graph construction and cultural research. However, the lack of annotated datasets for ICH has limited progress in this area. To address this, we present a Chinese dataset specifically designed for NER tasks in the ICH domain, covering key entity categories such as heritage items, inheritors, and material. Additionally, we propose an NER model that integrates RoBERTa for feature representation, the Kolmogorov-Arnold Network (KAN) for extracting complex entity patterns, and a conditional random field (CRF) for sequence labeling. Experimental results demonstrate the model's effectiveness in capturing the intricate semantic dependencies in ICH texts. The dataset and model contribute to improving entity recognition in the ICH domain, facilitating the structured representation of cultural heritage knowledge. This work provides a valuable resource for further research in information extraction, digital preservation, and cultural heritage studies.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.