Additional knowledge can be inferred on top of the standardized knowledge graph, generating insights for business and consumer analytics. For example, by conducting OLAP to selectively aggregate graph data from different points of view, we can generate real-time insights such as the number of members who have a given skill in a given location (supply), the number of job hires requiring a given skill in that same location (demand), and finally the sophisticated skill gap after considering both supply and demand ends. We can also constrain the data analytics into a certain time range for fetching retrospective insights. The below figure lists the top ten most in-demand soft skills that can help job seekers stand out from other candidates based on data analytics on member profile updates between June 2014 and June 2015.

Insights help leaders and sales make business decisions, and increase member engagement with LinkedIn. For example, the above insights encourage members to add those soft skills to their profiles or learn them in LinkedIn online courses.

The discovery of data insights from a standardized knowledge graph is an experience-driven data mining process. It can disclose previously undiscerned relationships between entities, which is thus another way of completing the LinkedIn knowledge graph. As shown in the below figure, the above insight example defines a new type of entity relationship from member to skills (“skills you may want to learn”).

« Generating analytics and insights from a knowledge graph »

A quote saved on Jan. 10, 2018.


Top related keywords - double-click to view: