  Google and other search engines feature the entity search by representing a
knowledge card summarizing related facts about the user-supplied entity.
However, the knowledge card is limited to certain entities that have a Wiki
page or an entry in encyclopedias such as Freebase. The current encyclopedias
are limited to highly popular entities, which are far fewer compared with the
emerging entities. Despite the availability of knowledge about the emerging
entities on the search results, yet there are no approaches to capture,
abstract, summerize, fuse, and validate fragmented pieces of knowledge about
them. Thus, in this paper, we develop approaches to capture two types of
knowledge about the emerging entities from a corpus extended from top-n search
snippets of a given emerging entity. The first kind of knowledge identifies the
role(s) of the emerging entity as, e.g., who is s/he? The second kind captures
the entities closely associated with the emerging entity. As the testbed, we
considered a collection of 20 emerging entities and 20 popular entities as the
ground truth. Our approach is an unsupervised approach based on text analysis
and entity embeddings. Our experimental studies show promising results as the
accuracy of more than $87\%$ for recognizing entities and $75\%$ for ranking
them. Besides $87\%$ of the entailed types were recognizable. Our testbed and
source code is available on Github
https://github.com/sunnyUD/research_source_code.
