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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: 2648040
-    num_examples: 17823
-  download_size: 394086
-  dataset_size: 2648040
 ---
 
 # Schema.org
@@ -49,8 +24,8 @@ Vocabulary schemas for structured data on the web
 
 ### Dataset Summary
 
-This dataset contains RDF triples from Schema.org converted to HuggingFace dataset format
-for easy use in machine learning pipelines.
+This dataset contains RDF triples from Schema.org converted to HuggingFace
+dataset format for easy use in machine learning pipelines.
 
 - **Format:** Originally turtle, converted to HuggingFace Dataset
 - **Size:** 0.01 GB (extracted)
@@ -67,9 +42,10 @@ Ontology understanding, vocabulary learning, small-scale testing
 
 ## Dataset Format: Lossless RDF Representation
 
-This dataset uses a **standard lossless format** for representing RDF (Resource Description Framework)
-data in HuggingFace Datasets. All semantic information from the original RDF knowledge graph is preserved,
-enabling perfect round-trip conversion between RDF and HuggingFace formats.
+This dataset uses a **standard lossless format** for representing RDF (Resource
+Description Framework) data in HuggingFace Datasets. All semantic information from
+the original RDF knowledge graph is preserved, enabling perfect round-trip conversion
+between RDF and HuggingFace formats.
 
 ### Schema
 
@@ -77,11 +53,16 @@ Each RDF triple is represented as a row with **6 fields**:
 
 | Field | Type | Description | Example |
 |-------|------|-------------|---------|
-| `subject` | string | Subject of the triple (URI or blank node) | `"http://schema.org/Person"` |
-| `predicate` | string | Predicate URI | `"http://www.w3.org/1999/02/22-rdf-syntax-ns#type"` |
-| `object` | string | Object of the triple | `"John Doe"` or `"http://schema.org/Thing"` |
-| `object_type` | string | Type of object: `"uri"`, `"literal"`, or `"blank_node"` | `"literal"` |
-| `object_datatype` | string | XSD datatype URI (for typed literals) | `"http://www.w3.org/2001/XMLSchema#integer"` |
+| `subject` | string | Subject of the triple (URI or blank node) |
+  `"http://schema.org/Person"` |
+| `predicate` | string | Predicate URI |
+  `"http://www.w3.org/1999/02/22-rdf-syntax-ns#type"` |
+| `object` | string | Object of the triple |
+  `"John Doe"` or `"http://schema.org/Thing"` |
+| `object_type` | string | Type of object: `"uri"`, `"literal"`, or
+  `"blank_node"` | `"literal"` |
+| `object_datatype` | string | XSD datatype URI (for typed literals) |
+  `"http://www.w3.org/2001/XMLSchema#integer"` |
 | `object_language` | string | Language tag (for language-tagged literals) | `"en"` |
 
 ### Example: RDF Triple Representation
@@ -131,7 +112,8 @@ for row in data:
 
 ### Converting Back to RDF
 
-The dataset can be converted back to any RDF format (Turtle, N-Triples, RDF/XML, etc.) with **zero information loss**:
+The dataset can be converted back to any RDF format (Turtle, N-Triples, RDF/XML,
+etc.) with **zero information loss**:
 
 ```python
 from datasets import load_dataset
@@ -186,11 +168,14 @@ This format preserves **100% of RDF information**:
 
 - ✅ **URIs**: Exact string representation preserved
 - ✅ **Literals**: Full text content preserved
-- ✅ **Datatypes**: XSD and custom datatypes preserved (e.g., `xsd:integer`, `xsd:dateTime`)
+- ✅ **Datatypes**: XSD and custom datatypes preserved (e.g., `xsd:integer`,
+  `xsd:dateTime`)
 - ✅ **Language Tags**: BCP 47 language tags preserved (e.g., `@en`, `@fr`, `@ja`)
-- ✅ **Blank Nodes**: Node structure preserved (identifiers may change but graph isomorphism maintained)
+- ✅ **Blank Nodes**: Node structure preserved (identifiers may change but graph
+  isomorphism maintained)
 
-**Round-trip guarantee**: Original RDF → HuggingFace → Reconstructed RDF produces **semantically identical** graphs.
+**Round-trip guarantee**: Original RDF → HuggingFace → Reconstructed RDF produces
+**semantically identical** graphs.
 
 ### Querying the Dataset
 
@@ -223,7 +208,8 @@ print(df["predicate"].value_counts())
 
 ### Dataset Format
 
-The dataset contains all triples in a single **data** split, suitable 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
@@ -235,7 +221,7 @@ The dataset contains all triples in a single **data** split, suitable for machin
 
 For complete technical documentation of the RDF-to-HuggingFace format, see:
 
-📖 [RDF to HuggingFace Format Specification](https://github.com/CleverThis/cleverernie/blob/master/docs/rdf_huggingface_format_specification.md)
+� [RDF to HuggingFace Format Specification](https://github.com/CleverThis/cleverernie/blob/master/docs/rdf_huggingface_format_specification.md)
 
 The specification includes:
 - Detailed schema definition
@@ -250,7 +236,7 @@ The specification includes:
 - **Original Size**: 0.01 GB
 - **Conversion Tool**: [CleverErnie RDF Pipeline](https://github.com/CleverThis/cleverernie)
 - **Format Version**: 1.0
-- **Conversion Date**: 2025-11-06
+- **Conversion Date**: 2025-12-18
 
 
 ## Citation
@@ -267,7 +253,7 @@ This dataset was prepared using the CleverErnie GISM framework:
 
 ```bash
 # Download original dataset
-cleverernie download-dataset -d schema-org
+python scripts/rdf_dataset_downloader.py schema-org -o datasets/
 
 # Convert to HuggingFace format
 python scripts/convert_rdf_to_hf_dataset.py \