philippesaade/Wikidata_Vectors_0.2

Wikidata Entity Embeddings 0.2

Dataset Summary

Wikidata Entity Embeddings is a dataset of embedding vectors for Wikidata entities. Each vector represents a Wikidata item (Q...) or property (P...) based on textual information extracted from Wikidata.

The dataset is part of the Wikidata Embedding Project, an initiative led by Wikimedia Deutschland in collaboration with Jina AI and IBM DataStax. The project provides a publicly accessible Wikidata Vector Database to enable semantic search and support the mission-aligned, open-source AI community in building applications on top of Wikidata.

A publicly accessible API is available for querying the vector database containing these embeddings:

• API: wd-vectordb.wmcloud.org
• Documentation: wd-vectordb.wmcloud.org/docs
• Project Page: wikidata.org/wiki/Wikidata:Vector_Database

Additional details about the embedding pipeline and infrastructure are available on the project page.

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Dataset Structure

Dataset Statistics

The dataset contains:

• 44 million vectors
• 23 million unique Wikidata entities (entities linked to at least one Wikipedia page)
• 512-dimensional embeddings
• Languages: English (en), French (fr), German (de), Arabic (ar)

Language	Vectors	Unique WD Items
English	21,127,781	21,094,882
French	10,662,599	10,631,982
German	9,793,965	9,773,883
Arabic	2,986,814	2,974,632

Embeddings are generated separately for each language using textual representations of entities. Therefore, the same entity may appear multiple times with different language-specific embeddings.

Additional languages will be added in future releases.

Data Fields

The dataset is organized by language and stored as Parquet chunks: data/<lang>/chunk-0.parquet

Each shard contains the following columns:

Field	Type	Description
id	string	Unique row identifier
vector	string	Base64-encoded float32 embedding vector
lang	string	Language used to generate the embedding
wdid	string	Wikidata identifier (QID or PID)

How to Decode Vectors

The vector column is encoded as base64 representations of little-endian float32 arrays. Example encoding and decoding:

from datasets import load_dataset
import base64
import numpy as np

LANGUAGE = 'en'

def encode_vector(vector_arr: np.ndarray) -> str:
    binary_data = vector_arr.tobytes()
    return base64.b64encode(binary_data).decode('utf8')

def decode_vector(vector_b64: str) -> np.ndarray:
    binary_data = base64.b64decode(vector_b64)
    return np.frombuffer(binary_data, dtype="<f4")

ds = load_dataset(
    "philippesaade/Wikidata_Vectors_0.2",
    data_files=f"data/{LANGUAGE}/*.parquet",
    streaming=True,
)

# Iterate over the dataset:
# for example in ds:
#     vector = decode_vector(example["vector"])
#     print("id:", example["id"])
#     print("wdid:", example["wdid"])
#     print("lang:", example["lang"])
#     print("vector shape:", vector.shape)
#     print("first 5 values:", vector[:5])

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Dataset Creation

Source Data

The dataset is derived from Wikidata, the world’s largest free and open knowledge graph that can be read and edited by both humans and machines. It provides structured data for Wikimedia projects such as Wikipedia, Wikisource, WikiCommons, WikiCite, and Wikivoyage, as well as applications and services outside Wikimedia. Launched in 2012 by Wikimedia Deutschland and the Wikimedia Foundation, Wikidata has grown into the world’s largest collaboratively edited knowledge graph, containing over 112 million structured data objects. It is maintained by a community of 24,000+ monthly contributors and is available in over 300 languages.

Entity Selection

Entities are included only if they satisfy the following criteria:

1. The entity has at least one Wikipedia sitelink.
2. The entity has a label in the target language (or in the multilingual mul language).
3. The entity has either:
   • A description in the target language (or in ‘mul’), or
   • At least one statement associated with the entity.
4. Our team has the capacity to prioritise, extract, transform, and load the specified language.

Vector Generation

The embeddings were computed using jina-embeddings-v3, a multilingual embedding model from Jina AI. For this dataset, vectors were generated with:

• task: retrieval.passage
• embedding size: 512

For each entity, a textual representation was constructed from its label, description, and serialised statements and encoded into a vector. These textual representations were generated using a pipeline available via the Wikidata Textifier API (docs). Further details about the embedding pipeline, text construction, and infrastructure used to generate the vectors are available on the project page.

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Limitations

• The embedding model is not knowledge graph–native. Embeddings are generated from flattened, textual representations of entities rather than directly from the graph structure of Wikidata. This implies that structural relationships in the knowledge graph are captured only indirectly through their textual representations.
• Only entities with at least one Wikipedia sitelink and sufficient textual information are included (see above).
• Data updates are limited to the May 07, 2026, Wikidata Data Dump, and changes after this date are not reflected.