Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-pretty_name: Wikidata Entity Embeddings 1.0
+pretty_name: Wikidata Entity Embeddings 0.2
 private: true
 license: cc0-1.0
 language:
@@ -29,17 +29,18 @@ models:
 - jinaai/jina-embeddings-v3
 ---
 
-# Wikidata Entity Embeddings 1.0
+# 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](https://www.wikidata.org/wiki/Wikidata:Embedding_Project)**, an initiative led by **Wikimedia Deutschland** in collaboration with **[Jina AI](https://jina.ai/)** and **[IBM DataStax](https://www.ibm.com/products/datastax)**. The project provides a publicly accessible **[Wikidata Vector Database](https://www.wikidata.org/wiki/Wikidata:Vector_Database)** to enable semantic search and support the open source AI community in building applications on top of Wikidata.
+The dataset is part of the **[Wikidata Embedding Project](https://www.wikidata.org/wiki/Wikidata:Embedding_Project)**, an initiative led by **Wikimedia Deutschland** in collaboration with **[Jina AI](https://jina.ai/)** and **[IBM DataStax](https://www.ibm.com/products/datastax)**. The project provides a publicly accessible **[Wikidata Vector Database](https://www.wikidata.org/wiki/Wikidata:Vector_Database)** to enable semantic search and support the mission-aligned, open-source AI community in building applications on top of Wikidata.
 
-A public API is available for querying the vector database containing these embeddings:
+A publicly accessible API is available for querying the vector database containing these embeddings:
 * **API**: [wd-vectordb.wmcloud.org](https://wd-vectordb.wmcloud.org/)
 * **Documentation**: [wd-vectordb.wmcloud.org/docs](https://wd-vectordb.wmcloud.org/docs)
+* **Project Page**: [wikidata.org/wiki/Wikidata:Vector_Database](https://www.wikidata.org/wiki/Wikidata:Vector_Database)
 
 Additional details about the embedding pipeline and infrastructure are available on the [project page](https://www.wikidata.org/wiki/Wikidata:Vector_Database).
 
@@ -54,7 +55,7 @@ The dataset contains:
 - **512-dimensional embeddings**
 - **Languages:** English (en), French (fr), German (de), Arabic (ar)
 
-| Language | Vectors | Unique Items |
+| Language | Vectors | Unique WD Items |
 |---|---:|---:|
 | English | 21,127,781 | 21,094,882 |
 | French | 10,662,599 | 10,631,982 |
@@ -80,9 +81,12 @@ Each shard contains the following columns:
 The `vector` column is encoded as base64 representations of little-endian float32 arrays.
 Example encoding and decoding:
 ```python
+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')
@@ -91,10 +95,20 @@ def decode_vector(vector_b64: str) -> np.ndarray:
     binary_data = base64.b64decode(vector_b64)
     return np.frombuffer(binary_data, dtype="<f4")
 
-# Example:
-# arr = decode_vector(example_row["vector"])
-# print(arr.shape)
-# print(arr[:10])
+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])
 ```
 
 ---
@@ -102,7 +116,7 @@ def decode_vector(vector_b64: str) -> np.ndarray:
 ## Dataset Creation
 
 ### Source Data
-The dataset is derived from [Wikidata](https://www.wikidata.org/), a free and open knowledge graph that can be read and edited by both humans and machines. It provides structured data for Wikimedia projects like Wikipedia, Wikisource, and Wikivoyage as well as applications and services outside Wikimedia. Launched in 2012 by Wikimedia Deutschland, 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.
+The dataset is derived from [Wikidata](https://www.wikidata.org/), 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:
@@ -111,8 +125,7 @@ Entities are included only if they satisfy the following criteria:
 3. The entity has either:
     - A description in the target language (or in ‘mul’), or
     - At least one statement associated with the entity.
-
-Because these conditions are evaluated per language, an entity may have embeddings for some languages but not others.
+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](https://huggingface.co/jinaai/jina-embeddings-v3), a multilingual embedding model from [Jina AI](https://jina.ai/). For this dataset, vectors were generated with:
@@ -120,11 +133,11 @@ The embeddings were computed using [jina-embeddings-v3](https://huggingface.co/j
 * task: `retrieval.passage`
 * embedding size: `512`
 
-For each entity, a textual representation constructed from its label, description, and serialized statements is encoded into a vector. Further details about the embedding pipeline, text construction, and infrastructure used to generate the vectors are available on the [project page](https://www.wikidata.org/wiki/Wikidata:Vector_Database).
+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](https://wd-textify.wmcloud.org/) ([docs](https://wd-textify.wmcloud.org/docs)). Further details about the embedding pipeline, text construction, and infrastructure used to generate the vectors are available on the [project page](https://www.wikidata.org/wiki/Wikidata:Vector_Database).
 
 ---
 
 ## Limitations
-- The embedding model is not knowledge graph–native. Embeddings are generated from textual representations of entities rather than directly from the graph structure of Wikidata, meaning structural relationships in the knowledge graph are only captured indirectly through their textual representation.
-- Only entities with at least one Wikipedia sitelink and sufficient textual information are included.
-- Data updates are limited to the September 18, 2024 dump, and changes after this date are not reflected.
+- 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 September 18, 2024, Wikidata Data Dump, and changes after this date are not reflected.