At the Semantic Web Blog we’ve regularly covered the increasingly promising world of jobs in our sector (see here). But Semantic Web technologies are being put to work to help job-seekers in every industry, whether or not they can tell the difference between RDF and RFP, or OWL and OTB.

One of the recent efforts hails from start-up Careerimp, which lately unveiled its semantically intelligent resume builder Resunate that has roots in research begun at Carnegie Mellon. The idea is to make it easier for people who’ve identified a position they want to best present themselves to the applicant tracking systems that are the first hurdle to overcome in order to get a live person’s attention.

Right now, it can take hours or even days to tinker a resume to those ends, while the old-school approach of building one solid resume that can suit any job hunt is quickly going by the wayside. We’re a society of specialists, and that means your resume has to help specialize you for each particular job, too.

“We want job seekers to see how their resumes will be treated on the employer side,” says Careerimp CEO Ayan Kishore. Over 80 percent of employers in the United States now use large applicant tracking systems to aid their personnel hunts, says Mona Abdel-Halim, director of sales and marketing. Traditionally these tools have been Boolean in nature, but many now have themselves adopted semantic technologies – Monster with its Power Resume Search and even LinkedIn Jobs, for example.

“Employers can easily rank resumes according to a job description to say who is the most relevant candidate, and we are trying to reverse that in some ways,” says Kishore. “Here is the job description and how can we convert your resume into a higher-ranking one.”

How indeed. Resunate’s solution starts with trying to make sure job candidates’ data makes it into an applicant tracking system without the key points getting lost. Sixty percent of data scanned into a database winds up adrift because it’s not parsed into the proper structure, the company says.

To that end, its first step is to focus a resume, starting with a semantic analysis of the job description to determine meanings of required expertise and the like. It follows that up with a semantic analysis of experiences and the bullet points on users’ own resumes to come up with concepts for each, which it then stacks up against those it derived from the job description, resulting in a score and ranking of how close a match each is to those concepts on the job description. From there, it condenses the resume to the length the user requests, displaying only the key experience bullet points that fit within the job-match resume threshold it determines from the length requested. It does the same for each new job a user is aiming at. “Every time you apply for a different job, we match your profile content against the job to figure out the most relevant content to put into a nice, precise resume,” says Kishore.

Even if the employer doesn’t use an automated applicant tracking system, where showcasing relevant skills should add up to higher rankings, the process still makes sense, says Abdel-Halim. “If they’re visually reviewing a huge stock of resumes, those that are more focused to the job description are easier to recall and more likely to be ranked higher,” she says. The company discovered this by working with more than 50 HR directors to understand their thinking.

Careerimp sees its semantic-derived focusing as a help to young and old job seekers alike. If you’ve been around the block a few times, you probably have lots of accomplishments and positions to list – and a hard time figuring out which is the most relevant content for a specific job, so that the resume can make it past a system and into a human’s hands. And once there, those humans aren’t forced to read through data that isn’t pertinent to the job.  Entry-level candidates with little full-time paid experience also can use it to help them assess what unpaid positions or even coursework might be considered relevant to a particular job, which can help beef up their credentials.

The vendor says it’s conducted a blind study with employers that show that candidates whose resumes were focused with its system and reviewed through applicant tracking systems were twice as likely to be called in for an interview than when they weren’t.

Another benefit it says users get is that they can keep their entire work history in one private repository (vs. a public profile like LinkedIn), continually adding to it in real time as they gain new experiences. That’s a good way to remember accomplishments that might make for a good semantic match to a job someday which might otherwise be forgotten as time passes. Careerimp also is planning to provide features to encourage changing content so that resumes rank better against jobs.

It’s free to create an unlimited number of resumes and scan them up against 25 job descriptions. To go beyond that, the company offers one- and six-month and yearly subscription packages (note: the average unemployment term for job seekers today is over six months).

Kishore says there’s a match to be made between this solution and job boards like Monster.com, which itself recently updated its semantic job search function. “They can convert users’ resumes into a structured view but they still lose a lot of information,” he says. “If our handoff to them can provide almost compete information for them to convert into a structured view, that is a win for job seekers, employers, and the job board.”

Next week, find out more about what Monster’s been up to with its semantic technologies for job seekers and fillers.