Now-a-day Wikipedia is becoming the main source of data for analyzing, researching and finding insights from it. Many researchers are for this reason interested about Wikipedia data. Understand the relationship between entities according to a given set of entities which we called seed entities. Finding insights from them, it is also very important to obtain more related entities and analyze them. Entity resolution is a problem that arises in many information integration scenarios. To fulfilling this purpose visualizing entities through graph has an increasing demand and interest among researchers. To analyzing this data as a source our main goal objective is mining entities from events and study how to effectively use crowdsourcing techniques to generate an automated trustable entity graph. Based on this foundation we develop a model for generating entities and inside the page based on the link entity it extends the input seed entity. We develop models and methods that find out the co-occurences between entities based on their events and automatically generate the entity graph. Also this produces a word cloud representation according to user's given input.