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Paper

Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pretrained language models

by Independent / Community 005d2cf3f239bd2803d5653cc86640bda33db31a

Free2AITools Nexus Index

63.2

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 70

P: Popularity 45

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Objective: To construct the knowledge graph of Shen Nong Ben Cao Jing, analyze basic knowledge of materia medica, explore implicit knowledge, and conduct visualization display, as well as provide methodological references for the study of ancient traditional Chinese medicine (TCM) books. Background: The research and utilization of ancient TCM books are relatively limited at present. With the rapid development of artificial intelligence, knowledge graph-related technology has brought light to ...

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Semantic Scholar 4 Citations

Paper Information Summary
Entity Passport
Registry ID	005d2cf3f239bd2803d5653cc86640bda33db31a
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{005d2cf3f239bd2803d5653cc86640bda33db31a,
  author = {Unknown},
  title = {Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pretrained language models Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/005d2cf3f239bd2803d5653cc86640bda33db31a}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pretrained language models [Paper]. Free2AITools. https://api.semanticscholar.org/005d2cf3f239bd2803d5653cc86640bda33db31a

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 70

Popularity (P) 45

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pretrained language models: Authority (A:70), Popularity (P:45), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Objective: To construct the knowledge graph of Shen Nong Ben Cao Jing, analyze basic knowledge of materia medica, explore implicit knowledge, and conduct visualization display, as well as provide methodological references for the study of ancient traditional Chinese medicine (TCM) books. Background: The research and utilization of ancient TCM books are relatively limited at present. With the rapid development of artificial intelligence, knowledge graph-related technology has brought light to ..."

❝ Cite Node

@article{Unknown2026Chinese,
  title={Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pretrained language models},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈4CitationsSemantic Scholar

🏛️70AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️knowledge retrievalField

📦Data Source: semantic_scholar

🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

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Technical metadata sourced from upstream repositories.

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 4

Data indexed from public sources. Updated daily.