Unlike the skills needed in finding the answers to trivia questions, when it comes to finding and identifying qualified and talented people based on their resumes and social media profiles and updates, the information is often incomplete, and in many cases, critical bits of identifying data are simply not present. For example, how do you find someone with "Spring MVC" experience when many people don’t mention it on their resume, nor on LinkedIn, Twitter, blogs, etc.?
There is an entire world of Human Capital Data in LinkedIn – direct keyword, title, and even concept/relational search methods, used by humans or algorithms, that can only retrieve results based on existing text. Quite simply – if the text isn’t there to be retrieved or analyzed, a semantic search/NLP algorithm can’t do anything with it. There is much more to high-level sourcing than keyword and title search/match. Good recruiters really do “read between the lines” of both the job description and requirements as well as the human capital data they are searching for and analyzing.
There have been semantic solutions on the market for quite some time that can do keyword, title and concept matching reasonably well (as well as some that claim to, but don’t). The issue with those solutions that no one seems to (or wants to) realize is that they have limitations – they find some matches, exclude some, and bury others. The real question is who, how, and why are some matches found and ranked highly, while others are excluded, and others ranked lowly but actually represent the best talent?