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README.md

@@ -12,31 +12,6 @@ tags:
 - triples
 size_categories:
 - 1K<n<10K
-configs:
-- config_name: default
-  data_files:
-  - split: data
-    path: data/data-*
-dataset_info:
-  features:
-  - name: subject
-    dtype: string
-  - name: predicate
-    dtype: string
-  - name: object
-    dtype: string
-  - name: object_type
-    dtype: string
-  - name: object_datatype
-    dtype: string
-  - name: object_language
-    dtype: string
-  splits:
-  - name: data
-    num_bytes: 622920511
-    num_examples: 3854624
-  download_size: 106525981
-  dataset_size: 622920511
 ---
 
 # WordNet RDF
@@ -111,12 +86,11 @@ from datasets import load_dataset
 # Load the dataset
 dataset = load_dataset("CleverThis/wordnet")
 
-# Access train and test splits
-train_data = dataset["train"]
-test_data = dataset["test"]
+# Access the data
+data = dataset["data"]
 
 # Iterate over triples
-for row in train_data:
+for row in data:
     subject = row["subject"]
     predicate = row["predicate"]
     obj = row["object"]
@@ -138,7 +112,7 @@ The dataset can be converted back to any RDF format (Turtle, N-Triples, RDF/XML,
 from datasets import load_dataset
 from rdflib import Graph, URIRef, Literal, BNode
 
-def convert_to_rdf(dataset_name, output_file="output.ttl", split="train"):
+def convert_to_rdf(dataset_name, output_file="output.ttl", split="data"):
     """Convert HuggingFace dataset back to RDF Turtle format."""
     # Load dataset
     dataset = load_dataset(dataset_name)
@@ -203,28 +177,28 @@ from datasets import load_dataset
 dataset = load_dataset("CleverThis/wordnet")
 
 # Find all triples with English literals
-english_literals = dataset["train"].filter(
+english_literals = dataset["data"].filter(
     lambda x: x["object_type"] == "literal" and x["object_language"] == "en"
 )
 print(f"Found {len(english_literals)} English literals")
 
 # Find all rdf:type statements
-type_statements = dataset["train"].filter(
+type_statements = dataset["data"].filter(
     lambda x: "rdf-syntax-ns#type" in x["predicate"]
 )
 print(f"Found {len(type_statements)} type statements")
 
 # Convert to Pandas for analysis
 import pandas as pd
-df = dataset["train"].to_pandas()
+df = dataset["data"].to_pandas()
 
 # Analyze predicate distribution
 print(df["predicate"].value_counts())
 ```
 
-### Dataset Splits
+### Dataset Format
 
-The dataset is split into **train** (95%) and **test** (5%) sets for machine learning tasks such as:
+The dataset contains all triples in a single **data** split, suitable for machine learning tasks such as:
 
 - Knowledge graph completion
 - Link prediction
@@ -251,7 +225,7 @@ The specification includes:
 - **Original Size**: 0.21 GB
 - **Conversion Tool**: [CleverErnie RDF Pipeline](https://github.com/CleverThis/cleverernie)
 - **Format Version**: 1.0
-- **Conversion Date**: 2025-11-02
+- **Conversion Date**: 2025-11-05
 
 
 ## Citation
@@ -290,7 +264,7 @@ https://en-word.net/static/english-wordnet-2024.ttl.gz
 
 - Converted using: [CleverErnie GISM](https://github.com/cleverthis/cleverernie)
 - Conversion script: `scripts/convert_rdf_to_hf_dataset.py`
-- Train/test split: 95%/5%
+- Dataset format: Single 'data' split with all triples
 
 ### Maintenance