Your skills and expertise are a large part of how you establish your professional identity, and the LinkedIn Skills team is working on a number of interesting problems in this domain.
One of the first problems that we have tackled is simply identifying what skills and areas of experties exist among LinkedIn members. One member may describe herself as a Ruby on Rails developer in her summary or list a position as an instructor teaching ballet and modern dance. We must be able to pick out these skills as we establish a standardized corpus. Other members may list experience with JSP (a common acronymn for Java Server Pages) or a server administration job using exchange (a reference to Microsoft Exchange). These references which use acronymns or are contextually dependent are a few examples of the complications for this task. Our team uses techniques from natural language processing, text mining, entity extraction and machine learning to address these challenges.
Creating the set of skills is just the first step though. We are using these skills to create products and data such as personalized set of skills suggestions that a member may add to his profile or a list of top people in machine learning. These are just a few of the ways that we are using skills both as part of our internal data and in member-facing products. We are actively exploring more ways to use skills to provide better personalized content to our members and create unique products that harness the insights we gain from the LinkedIn network.