<p>One start would be to use data from Wikidata. It has some information on Chinese companies (I suppose you are referring to companies listed on Chinese stock exchanges). For instance,  <a href="https://www.wikidata.org/wiki/Q831445" rel="nofollow noreferrer">https://www.wikidata.org/wiki/Q831445</a> displays information about Sinopec.</p>

<p>The data from Wikidata can be downloaded from the API, the large dumps files at <a href="https://dumps.wikimedia.org/wikidatawiki/" rel="nofollow noreferrer">https://dumps.wikimedia.org/wikidatawiki/</a> or the SPARQL endpoint at <a href="https://query.wikidata.org/" rel="nofollow noreferrer">https://query.wikidata.org/</a>.</p>

<p>You can get a list of companies listed on the Shenzhen Stock Exchange with the SPARQL query:</p>

<pre><code>SELECT 
  ?company ?companyLabel
  ?industry ?industryLabel
{
  ?company wdt:P414 wd:Q517750 .
  OPTIONAL { ?company wdt:P452 ?industry }
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en,zh". }
}
</code></pre>

<p>The result is (also) available at <a href="https://w.wiki/9DM" rel="nofollow noreferrer">https://w.wiki/9DM</a> . This result can be extended by modifying the query and it can be downloaded in various formats. With the DESCRIBE SPARQL keyword you can get the triple format that may be useful for the TransE algorithm, e.g., <code>DESCRIBE wd:Q831445</code> with the result at <a href="https://w.wiki/9DN" rel="nofollow noreferrer">https://w.wiki/9DN</a> .</p>

<p>It is possible to process the large dump files and make a knowledge graph embedding with Gensim's Word2Vec, see "Wembedder: Wikidata entity embedding web service" at <a href="https://arxiv.org/abs/1710.04099" rel="nofollow noreferrer">https://arxiv.org/abs/1710.04099</a> . You can explore one result of this approach with the Wembedder webapp, e.g., <a href="https://tools.wmflabs.org/wembedder/most-similar/Q51747" rel="nofollow noreferrer">https://tools.wmflabs.org/wembedder/most-similar/Q51747</a> displays the result of a "most similar" query in the knowledge graph embedding with Air China </p>
How to construct my own Datasets for TransE Algorithm in a specific knowledge graph
<p>now I am building a knowledge graph of the Chinese stock and want to build a news recommendation system. And I want to use TransE algorithm for the entity embedding and relationship embedding. ​But I do not have the dataset and don't know clearly how to build a dataset using my own knowledge graph?</p>