The April jobs report isn’t expected to have much in it that will show we’re on track to drive down the nation’s 8.8 percent unemployment rate, with gains predicted to be just a tad under 200,000 jobs. All the more reason, perhaps, to see if semantic technology can help job-seekers make a match.

Last week we looked at one semantic effort to improve the odds of getting a job. This week, we’ll take a look at Monster Worldwide, which just reported a $78,000 profit for the quarter ending March 31, up from a loss of $24.2 million a year earlier. Since acquiring job search service Trovix in 2008, Monster has been adapting the 6Sense semantic search technology it got in the deal to its employment marketplace, first with Power Resume Search for the HR set and then with job seekers themselves.

It debuted within the last month the most recent iteration of the latter, which adds to core search results additional job titles users might not have considered that are highly coordinated with their skills, as well as other skills that closely-linked jobs require. “The intention there is that if you look at the search problem from the job seeker standpoint, we noticed job seekers tend to get boxed in. they put in what they know,” says Javid Muhammedali, who leads Monster’s semantic search efforts. So they usually get back a limited number of precise, keyword-focused matches in return. “So they’re not looking at all the options that are available. As we build out variations going forward for job seekers, we give them variance for what they’re looking at so they can extend their horizons a bit.”

Users can click on the titles or skills Monster offers up, and the search is run again to expand the number of results. For example, searches for “sales,” thanks to the ontology behind this, also pull back titles like account rep or manager – something that mans a sales position even though the word doesn’t appear in the function title. “These are capabilities to help you go through the discovery process,” says Earl Rennison, VP at Monster Worldwide and formerly chairman and CTO of Trovix.”This give them a better understanding of what’s in their search results to help them navigate through the range of possibilities.”

Monster expects this to add up to more satisfied users and recruiters, too. Why? People using traditional search tools tend to apply to jobs that show up at the top of the list after they enter their keywords, rather than the most optimal job that may not be so immediately obvious. In fact, that optimal job may have been pushed to the bottom of the list because it was posted a few days ago and isn’t going to surface high again unless there’s an exact keyword match using traditional search methods. That means job seekers wind up “throwing their hat in the ring to see what happens and that causes downstream churn for recruiters,” says Muhammedali. “They get lots of job applications from people who are not qualified when they would prefer a fewer number of more qualified applicants.”

Right now Monster’s default is to give job seekers the semantic approach, though they can switch to its Boolean engine if they prefer. Monster’s also been working on applying its semantic technology to match someone’s job search with the skill sets noted in the jobs that come back from it, and then automatically pull up for the user’s attention relevant courses from its catalogue.

What’s Next For Semantic Search in the Jobs Space

“What we see from our internal data is tremendous improvement in efficiency for recruiters and job seekers from using semantic technologies,” says Muhammedali. “And the next level of development on this is to make it available on a cloud platform, so customers can use our search engines and search technology on their data.”

It’s provisioned its 6Sense semantic search stack on a virtual private cloud built on its own infrastructure, as hosts for HR departments’ resumes coming in from Monster, or via any internal application for storing that information. “They can unlock the power of the data that they have in their own databases,” he says – bringing the semantic search they get on Monster.com’s resume contingent to their own internal data. It includes talent matching, where they can take a stack of applicants who apply to a job and match them instantly against the requirements of the job.

The new Monster cloud service takes the resume document the job seeker has submitted and extracts data to transform it into a more structured and standardized detail view of the candidate.  That includes creating a profile of skills they acquired in their careers across multiple jobs – for instance, it can identify from a  candidate’s resume that he has worked on Java a couple of years ago and .Net and other programming technologies more recently to count up the total number of years of experience in the field overall, and how recent the experience is in each skill.

“It’s an Xray view into resume data. Because we have that data we use that in search instances where we tell [employer] customers why a person is a good match, and we have built out advanced reporting and analytics tools,” Muhammedali says. “So not only can HR find a qualified person sitting in its resume database, but after you come back from a job fair with 300 resumes you can now tell what those candidates were.”

The cloud service is in beta now with a handful of customers and is planned for launch later this year.

The cloud service has enormous potential even so far as it relates to current employees at companies, given that the employee databases people have now are poorly understood, Rennison says. “If a company wants to find someone [in-house] with certain skills to assign to projects or for a succession plan, they’re hard- pressed to give answers of who has these skills,” he says. “We see as the next frontier to get further into comapnies and help them understand the employee base better.”

Monster also expects to further globalize Power Resume Search, currently launched in the US, Canada, the U.K., Australia and France. Germany is coming in the next few months.