Datasets:

vipinpalhugging
/

wordnet_testing_123

subject stringlengths 20 185	predicate stringclasses 53 values	object stringlengths 1 508	object_type stringclasses 3 values	object_datatype stringclasses 1 value	object_language stringclasses 1 value
https://en-word.net/id/oewn-00353817-s	https://globalwordnet.github.io/schemas/wn#similar	https://en-word.net/id/oewn-00353228-a	uri	null	null
https://en-word.net/id/oewn-00409737-a	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db17727	blank_node	null	null
ndb6c05ae842b42a8ae1cae4ddee2198db19469	http://www.w3.org/1999/02/22-rdf-syntax-ns#value	he came off second-best	literal	null	en
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/snuggling#snuggling-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Navy_SEAL#Navy_SEAL-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/boskopoid#boskopoid-a	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/G.I.#G.I.-v	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/loather#loather-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/order_Opuntiales#order_Opuntiales-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/chewing#chewing-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Mary_Queen_of_Scots#Mary_Queen_of_Scots-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/dappled-grey#dappled-grey-n	uri	null	null
https://en-word.net/id/oewn-00349300-a	http://www.w3.org/1999/02/22-rdf-syntax-ns#type	http://www.w3.org/ns/lemon/ontolex#LexicalConcept	uri	null	null
https://en-word.net/id/oewn-00324097-s	https://globalwordnet.github.io/schemas/wn#partOfSpeech	https://globalwordnet.github.io/schemas/wn#adjective_satellite	uri	null	null
https://en-word.net/id/oewn-00339240-r	https://globalwordnet.github.io/schemas/wn#definition	ndb6c05ae842b42a8ae1cae4ddee2198db14829	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/oxynervonic_acid#oxynervonic_acid-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/weightlift#weightlift-v	uri	null	null
https://en-word.net/id/oewn-00422644-v	https://globalwordnet.github.io/schemas/wn#ili	http://globalwordnet.org/ili/i23834	uri	null	null
https://en-word.net/id/oewn-00270700-n	https://globalwordnet.github.io/schemas/wn#hypernym	https://en-word.net/id/oewn-00270102-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/white_lead#white_lead-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Salix_purpurea#Salix_purpurea-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/condylar#condylar-a	uri	null	null
ndb6c05ae842b42a8ae1cae4ddee2198db16456	http://www.w3.org/1999/02/22-rdf-syntax-ns#value	of brown tinged with hazel	literal	null	en
ndb6c05ae842b42a8ae1cae4ddee2198db4073	http://www.w3.org/1999/02/22-rdf-syntax-ns#value	he took a course in lifesaving	literal	null	en
https://en-word.net/id/oewn-00422440-n	https://globalwordnet.github.io/schemas/wn#hyponym	https://en-word.net/id/oewn-00423837-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/presuppose#presuppose-v	uri	null	null
https://en-word.net/id/oewn-00034685-s	http://www.w3.org/1999/02/22-rdf-syntax-ns#type	http://www.w3.org/ns/lemon/ontolex#LexicalConcept	uri	null	null
https://en-word.net/id/oewn-00035642-r	http://purl.org/dc/terms/subject	adv.all	literal	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Crab#Crab-n	uri	null	null
https://en-word.net/id/oewn-00098271-v	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db4239	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Camembert#Camembert-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/bun_divider#bun_divider-n	uri	null	null
https://en-word.net/id/oewn-00242413-r	https://globalwordnet.github.io/schemas/wn#definition	ndb6c05ae842b42a8ae1cae4ddee2198db10646	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/mortifying#mortifying-s	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Egretta_caerulea#Egretta_caerulea-n	uri	null	null
ndb6c05ae842b42a8ae1cae4ddee2198db11418	http://www.w3.org/1999/02/22-rdf-syntax-ns#value	inflict damage upon	literal	null	en
https://en-word.net/id/oewn-00086690-s	https://globalwordnet.github.io/schemas/wn#ili	http://globalwordnet.org/ili/i453	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/piroshki#piroshki-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/audio_system#audio_system-n	uri	null	null
https://en-word.net/id/oewn-00388211-r	http://www.w3.org/1999/02/22-rdf-syntax-ns#type	http://www.w3.org/ns/lemon/ontolex#LexicalConcept	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/recitative#recitative-n	uri	null	null
https://en-word.net/id/oewn-00067445-r	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db2839	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/loosen#loosen-v	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Caranx_crysos#Caranx_crysos-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/round_shot#round_shot-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/cay#cay-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/nightmare#nightmare-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/acid-base_balance#acid-base_balance-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/northernmost#northernmost-s	uri	null	null
https://en-word.net/id/oewn-00081836-r	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db3494	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/low-sodium_diet#low-sodium_diet-n	uri	null	null
https://en-word.net/id/oewn-00026546-r	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db1033	blank_node	null	null
https://en-word.net/id/oewn-00397038-s	https://globalwordnet.github.io/schemas/wn#ili	http://globalwordnet.org/ili/i2233	uri	null	null
https://en-word.net/id/oewn-00287084-n	https://globalwordnet.github.io/schemas/wn#definition	ndb6c05ae842b42a8ae1cae4ddee2198db12613	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/sentence_structure#sentence_structure-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Passer_domesticus#Passer_domesticus-n	uri	null	null
https://en-word.net/id/oewn-00032087-a	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db1250	blank_node	null	null
https://en-word.net/id/oewn-00219478-r	https://globalwordnet.github.io/schemas/wn#definition	ndb6c05ae842b42a8ae1cae4ddee2198db9601	blank_node	null	null
https://en-word.net/id/oewn-00370095-v	http://www.w3.org/2004/02/skos/core#inScheme	https://en-word.net/	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/ammonia_water#ammonia_water-n	uri	null	null
https://en-word.net/id/oewn-00195256-v	https://globalwordnet.github.io/schemas/wn#hypernym	https://en-word.net/id/oewn-00195686-v	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/wordy#wordy-s	uri	null	null
https://en-word.net/id/oewn-00063834-n	https://globalwordnet.github.io/schemas/wn#partOfSpeech	https://globalwordnet.github.io/schemas/wn#noun	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/crystalline#crystalline-s	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/out#out-r	uri	null	null
https://en-word.net/id/oewn-00299938-n	https://globalwordnet.github.io/schemas/wn#ili	http://globalwordnet.org/ili/i36972	uri	null	null
https://en-word.net/id/oewn-00088039-a	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db3766	blank_node	null	null
https://en-word.net/id/oewn-00286014-a	http://www.w3.org/2004/02/skos/core#inScheme	https://en-word.net/	uri	null	null
https://en-word.net/id/oewn-00433834-r	https://globalwordnet.github.io/schemas/wn#definition	ndb6c05ae842b42a8ae1cae4ddee2198db18793	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/jauntiness#jauntiness-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/order_Corrodentia#order_Corrodentia-n	uri	null	null
https://en-word.net/id/oewn-00096402-v	http://www.w3.org/2004/02/skos/core#inScheme	https://en-word.net/	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Ctenocephalides#Ctenocephalides-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/William_Henry_Mauldin#William_Henry_Mauldin-n	uri	null	null
https://en-word.net/id/oewn-00355803-s	http://purl.org/dc/terms/subject	adj.all	literal	null	null
https://en-word.net/id/oewn-00379335-v	http://www.w3.org/1999/02/22-rdf-syntax-ns#type	http://www.w3.org/ns/lemon/ontolex#LexicalConcept	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Xinjiang_Uighur_Autonomous_Region#Xinjiang_Uighur_Autonomous_Region-n	uri	null	null
https://en-word.net/id/oewn-00325361-v	https://globalwordnet.github.io/schemas/wn#partOfSpeech	https://globalwordnet.github.io/schemas/wn#verb	uri	null	null
https://en-word.net/id/oewn-00442014-r	https://globalwordnet.github.io/schemas/wn#ili	http://globalwordnet.org/ili/i21171	uri	null	null
https://en-word.net/id/oewn-00407299-r	https://globalwordnet.github.io/schemas/wn#partOfSpeech	https://globalwordnet.github.io/schemas/wn#adverb	uri	null	null
https://en-word.net/id/oewn-00402267-n	http://www.w3.org/1999/02/22-rdf-syntax-ns#type	http://www.w3.org/ns/lemon/ontolex#LexicalConcept	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/benevolent#benevolent-a	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/cell_division#cell_division-n	uri	null	null
https://en-word.net/id/oewn-00219829-v	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db9612	blank_node	null	null
https://en-word.net/id/oewn-00211755-r	http://www.w3.org/1999/02/22-rdf-syntax-ns#type	http://www.w3.org/ns/lemon/ontolex#LexicalConcept	uri	null	null
https://en-word.net/id/oewn-00385677-s	https://globalwordnet.github.io/schemas/wn#partOfSpeech	https://globalwordnet.github.io/schemas/wn#adjective_satellite	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Edward_Vernon_Rickenbacker#Edward_Vernon_Rickenbacker-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/angiotensin_I#angiotensin_I-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/genus_Kohleria#genus_Kohleria-n	uri	null	null
https://en-word.net/id/oewn-00009997-a	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db339	blank_node	null	null
https://en-word.net/id/oewn-00139437-a	http://www.w3.org/2004/02/skos/core#inScheme	https://en-word.net/	uri	null	null
https://en-word.net/id/oewn-00302637-v	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db13291	blank_node	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/split_second#split_second-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/Sphagnales#Sphagnales-n	uri	null	null
https://en-word.net/	http://www.w3.org/ns/lemon/lime#entry	https://en-word.net/lemma/selective_service#selective_service-n	uri	null	null
ndb6c05ae842b42a8ae1cae4ddee2198db1016	http://www.w3.org/1999/02/22-rdf-syntax-ns#value	become tense, nervous, or uneasy	literal	null	en
ndb6c05ae842b42a8ae1cae4ddee2198db9642	http://www.w3.org/1999/02/22-rdf-syntax-ns#value	make sure the gear is engaged	literal	null	en
https://en-word.net/id/oewn-00420921-n	https://globalwordnet.github.io/schemas/wn#ili	http://globalwordnet.org/ili/i37611	uri	null	null
https://en-word.net/id/oewn-00015706-v	http://www.w3.org/2004/02/skos/core#inScheme	https://en-word.net/	uri	null	null
https://en-word.net/id/oewn-00300556-s	https://globalwordnet.github.io/schemas/wn#example	ndb6c05ae842b42a8ae1cae4ddee2198db13194	blank_node	null	null

End of preview. Expand in Data Studio

WordNet RDF

Dataset Description

Lexical database of semantic relations between words (English WordNet 2024)

Original Source: https://en-word.net/static/english-wordnet-2024.ttl.gz

Dataset Summary

This dataset contains RDF triples from WordNet RDF converted to HuggingFace dataset format for easy use in machine learning pipelines.

Format: Originally turtle, converted to HuggingFace Dataset
Size: 0.21 GB (extracted)
Entities: ~120K synsets
Triples: ~2M
Original License: CC BY 4.0

Recommended Use

Quick validation, linguistic relationships, small-scale testing

Notes: Compressed download, automatic extraction. English WordNet 2024 from en-word.net

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.

Schema

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"`
`object_language`	string	Language tag (for language-tagged literals)	`"en"`

Example: RDF Triple Representation

Original RDF (Turtle):

<http://example.org/John> <http://schema.org/name> "John Doe"@en .

HuggingFace Dataset Row:

{
  "subject": "http://example.org/John",
  "predicate": "http://schema.org/name",
  "object": "John Doe",
  "object_type": "literal",
  "object_datatype": None,
  "object_language": "en"
}

Loading the Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("CleverThis/wordnet")

# Access the data
data = dataset["data"]

# Iterate over triples
for row in data:
    subject = row["subject"]
    predicate = row["predicate"]
    obj = row["object"]
    obj_type = row["object_type"]

    print(f"Triple: ({subject}, {predicate}, {obj})")
    print(f"  Object type: {obj_type}")
    if row["object_language"]:
        print(f"  Language: {row['object_language']}")
    if row["object_datatype"]:
        print(f"  Datatype: {row['object_datatype']}")

Converting Back to RDF

The dataset can be converted back to any RDF format (Turtle, N-Triples, RDF/XML, etc.) with zero information loss:

from datasets import load_dataset
from rdflib import Graph, URIRef, Literal, BNode

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)

    # Create RDF graph
    graph = Graph()

    # Convert each row to RDF triple
    for row in dataset[split]:
        # Subject
        if row["subject"].startswith("_:"):
            subject = BNode(row["subject"][2:])
        else:
            subject = URIRef(row["subject"])

        # Predicate (always URI)
        predicate = URIRef(row["predicate"])

        # Object (depends on object_type)
        if row["object_type"] == "uri":
            obj = URIRef(row["object"])
        elif row["object_type"] == "blank_node":
            obj = BNode(row["object"][2:])
        elif row["object_type"] == "literal":
            if row["object_datatype"]:
                obj = Literal(row["object"], datatype=URIRef(row["object_datatype"]))
            elif row["object_language"]:
                obj = Literal(row["object"], lang=row["object_language"])
            else:
                obj = Literal(row["object"])

        graph.add((subject, predicate, obj))

    # Serialize to Turtle (or any RDF format)
    graph.serialize(output_file, format="turtle")
    print(f"Exported {len(graph)} triples to {output_file}")
    return graph

# Usage
graph = convert_to_rdf("CleverThis/wordnet", "reconstructed.ttl")

Information Preservation Guarantee

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)
✅ Language Tags: BCP 47 language tags preserved (e.g., @en, @fr, @ja)
✅ Blank Nodes: Node structure preserved (identifiers may change but graph isomorphism maintained)

Round-trip guarantee: Original RDF → HuggingFace → Reconstructed RDF produces semantically identical graphs.

Querying the Dataset

You can filter and query the dataset like any HuggingFace dataset:

from datasets import load_dataset

dataset = load_dataset("CleverThis/wordnet")

# Find all triples with English literals
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["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["data"].to_pandas()

# Analyze predicate distribution
print(df["predicate"].value_counts())

Dataset Format

The dataset contains all triples in a single data split, suitable for machine learning tasks such as:

Knowledge graph completion
Link prediction
Entity embedding
Relation extraction
Graph neural networks

Format Specification

For complete technical documentation of the RDF-to-HuggingFace format, see:

📖 RDF to HuggingFace Format Specification

The specification includes:

Detailed schema definition
All RDF node type mappings
Performance benchmarks
Edge cases and limitations
Complete code examples

Conversion Metadata

Source Format: turtle
Original Size: 0.21 GB
Conversion Tool: CleverErnie RDF Pipeline
Format Version: 1.0
Conversion Date: 2025-12-24

Citation

If you use this dataset, please cite the original source:

Original Dataset: WordNet RDF URL: https://en-word.net/static/english-wordnet-2024.ttl.gz License: CC BY 4.0

Dataset Preparation

This dataset was prepared using the CleverErnie GISM framework:

# Download original dataset
python scripts/rdf_dataset_downloader.py wordnet -o datasets/

# Convert to HuggingFace format
python scripts/convert_rdf_to_hf_dataset_unified.py \
    datasets/wordnet/[file] \
    hf_datasets/wordnet \
    --format turtle \
    --strategy auto

# Upload to HuggingFace Hub
python scripts/upload_all_datasets.py --dataset wordnet

Additional Information

Original Source

https://en-word.net/static/english-wordnet-2024.ttl.gz

Conversion Details

Converted using: CleverErnie GISM
Conversion script: scripts/convert_rdf_to_hf_dataset_unified.py
Dataset format: Single 'data' split with all triples
Strategy: Auto-selected based on dataset size and format

Maintenance

This dataset is maintained by the CleverThis organization.

Downloads last month: 5

Size of downloaded dataset files:

84.8 MB

Size of the auto-converted Parquet files:

84.8 MB

Number of rows:

3,854,624