  The knowledge graph(KG) composed of entities with their descriptions and
attributes, and relationship between entities, is finding more and more
application scenarios in various natural language processing tasks. In a
typical knowledge graph like Wikidata, entities usually have a large number of
attributes, but it is difficult to know which ones are important. The
importance of attributes can be a valuable piece of information in various
applications spanning from information retrieval to natural language
generation. In this paper, we propose a general method of using external user
generated text data to evaluate the relative importance of an entity's
attributes. To be more specific, we use the word/sub-word embedding techniques
to match the external textual data back to entities' attribute name and values
and rank the attributes by their matching cohesiveness. To our best knowledge,
this is the first work of applying vector based semantic matching to important
attribute identification, and our method outperforms the previous traditional
methods. We also apply the outcome of the detected important attributes to a
language generation task; compared with previous generated text, the new method
generates much more customized and informative messages.
