Editor’s note: This is an update from the SkillPages Engineering Team to the development of SkillGraph. See our previous post for background information on SkillGraph.
Language And Location Context
We are constantly improving and evolving SkillGraph to better match the most relevant skilled people to your need. One issue we face is that natural languages are ambiguous and imprecise. As inconvenient as this is for computers, it is a fact of life and must be managed. Different words have different meanings in different countries – an American user entering “football player” means something different to a British user entering these titles. Or a Mumbian using the acronym “TC” (meaning Ticket Checker) describes his job succinctly within an Indian context; however this acronym carries no relevance in other English speaking countries. Just like a brain uses context to disambiguate the meaning of a word, our classification engine capitalizes on the contextual information that a user provides.
In addition to typical methods of providing context, like language or location, we can also leverage the social underpinning of our platform. Take a user who describes themselves as a “Coach”. SkillGraph recognizes this as ambiguous, so then examines the skills/interests of the user’s social connections. Combining this knowledge with language and location, we can make a more confident classification as to whether the user is a Sports Coach, Corporate Trainer or Life Advisor.
This is what helps determine that our members get the most relevant opportunities for their skills. Another step towards creating the perfect relevance engine, but still plenty more development yet to come.