Create README.md

README.md

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+---
+pretty_name: DBpediaOntoTrain
+license: cc-by-4.0
+language:
+  - en
+tags:
+  - ontology
+  - owl
+  - turtle
+  - llm
+  - pretraining
+  - dbpedia
+size_categories:
+  - 1B<n<10B
+dataset_info:
+  features:
+    - name: file_name
+      type: string
+    - name: text
+      type: string
+    - name: PD
+      type: float
+    - name: NTR
+      type: float
+    - name: SC
+      type: float
+    - name: PD_norm
+      type: float
+    - name: NTR_norm
+      type: float
+    - name: SC_norm
+      type: float
+    - name: QS
+      type: float
+    - name: token_count
+      type: int
+    - name: token_count_acum
+      type: int
+    - name: percent_token_acum
+      type: float
+---
+
+
+# 🧠 DBpediaOntoTrain: A Quality-Segmented Ontology Dataset for LLM Pretraining
+
+## 📘 Overview
+
+**DBpediaOntoTrain** is a dataset of **1,766 OWL ontologies in Turtle format**, extracted from [DBpedia Archivo](https://archivo.dbpedia.org/) and prepared for **continual pretraining of Large Language Models (LLMs)** in ontology generation and completion tasks.
+
+Each ontology is analyzed using a set of **semantic quality metrics**, tokenized using the **LLaMA 3.2 tokenizer**, and sorted by **Quality Score (QS)**. The dataset includes **cumulative token counts and percentages**, allowing precise and reproducible slicing for quality-aware training.
+
+---
+
+## 📦 Dataset Contents
+
+- `data.json`: A JSON file where each entry contains:
+  - `File Name`: name of the ontology file (`.ttl`)
+  - `plain_text`: raw ontology content in Turtle syntax
+  - `PD`: Property Density by Class
+  - `NTR`: Non-Taxonomic Relations per Class
+  - `SC`: Subclasses per Class
+  - `PD_norm`, `NTR_norm`, `SC_norm`: min-max normalized versions of the above metrics
+  - `QS`: Quality Score (`PD_norm + NTR_norm + SC_norm`)
+  - `Token Count`: number of tokens computed using the **LLaMA 3.2 tokenizer**
+  - `Token Count Accumulation`: cumulative token count (sorted by descending QS)
+  - `Percentage of Token Count Accumulation`: running percentage of total tokens across all ontologies
+
+The dataset is sorted in descending order by Quality Score (`QS`), enabling easy extraction of quality-based subsets (e.g., Q1, Q1,2, etc.).
+
+---
+
+## 📊 Quality Metrics
+
+Each ontology is scored with:
+
+| Metric | Description |
+|--------|-------------|
+| **PD** | Property Density — properties per class |
+| **NTR** | Non-Taxonomic Relations — domain-specific relations per class |
+| **SC** | Subclass Count — hierarchical depth |
+| **QS** | Sum of normalized PD, NTR, SC |
+
+These metrics reflect **semantic modeling richness** rather than raw size.
+
+---
+
+## 🧪 Intended Use
+
+- Continual pretraining of LLMs on semantic data
+- Research in ontology learning, alignment, enrichment
+- Studying the effect of data quality on model generalization and reasoning
+
+This dataset supports the research study:
+
+> **Enhancing LLM Ontology Generation: The Role of Quality Semantic Data**  
+> Miquel Canal-Esteve, Yoan Gutiérrez, José Abreu-Salas (submitted to *ICT Express*, 2025)
+
+---
+
+## 🛠️ Tokenization
+
+- Tokenized using **LLaMA 3.2-1B tokenizer**
+- Total tokens: **1.25 billion**
+- Cumulative token fields allow extracting top-N% token subsets based on QS
+- Token overlap and LLM input chunking are described in the accompanying paper
+
+---
+
+## 💡 Reproducibility
+
+The repository includes:
+- Metric calculation scripts using [`rdflib`](https://github.com/RDFLib/rdflib)
+- Tokenization scripts with Hugging Face libraries
+- Pretraining configs and logs
+
+Repository:  
+👉 [https://github.com/miquelcanalesteve/LLM4Onto/](https://github.com/miquelcanalesteve/LLM4Onto/)
+
+---
+
+## 📄 Citation
+
+```bibtex
+@misc{canal2025dbpediaontotrain,
+  author    = {Miquel Canal-Esteve and Yoan Gutiérrez and José Abreu-Salas},
+  title     = {DBpediaOntoTrain: A Quality-Segmented Ontology Dataset for LLM Pretraining},
+  year      = {2025},
+  url       = {https://github.com/miquelcanalesteve/LLM4Onto/}
+}