You raise an excellent point that I did not address in the scope of my post – that parsing is critical when it comes to matching solutions, which it absolutely is. Better parsing results in more powerful, targeted and precise searching and matching.

What I also think is critical for prospective customers looking to implement a semantic matching solution is that they make sure they are able to modify taxonomies and ontologies to customize the solution for their specific needs – either themselves, or have the vendor do it for them under their direction. I haven’t seen any “out of the box” matching solutions that are already perfectly and completely relevant for every possible client. In fact – I believe that would be impossible.

You are correct that Resume Mirror uses Engenium – I should have had 2 lists: 1 for the search/match applications (such as Engenium), and 1 that includes parsing as well (such as Resume Mirror). I’ve worked with Engenium or a few years – I have never been impressed with its capability – in fact, I am quite disappointed with it.

And while you are correct that Sovren uses dtSearch as their text search engine, dtSearch, to the best of my knowledge, has no true semantic matching capability. I also could not find any mention of any semantic matching technology in dtSearch’s literature. http://www.dtsearch.com/

I’ve been using dtSearch as a stand alone product for a few years now, and it is a fantastic text search engine that effectively enables manual semantic search through variable term weighting and configurable proximity searching, along with some fuzzy/phonic capability (which I don’t use because it doesn’t increase relevancy in my experience). I am also currently working with Sovren – Sovren provides the Semantic Matching Engine powered by the data extraction, classification, and tagging technologies of the Sovren Resume/CV Parser, and simply uses dtSearch as the text search engine, as far as I understand it.

Do you have any other vendors you recommend I add to the list?

Thanks again for your insightful comment!

Full disclosure: HireAbility is in the resume parsing business not the matching business. We keep a vendor neutral approach and integrate with the best matching technology our clients choose. Sometimes that’s their own homegrown solutions. We believe better ranked results from matching solutions come from searching on the tagged data that parsers provide. So in theory: the better the parser, the better the data, the better the match. That’s why we choose to keep our focus on continuous advancement of our parser.

I agree with you that evaluations of accuracy can never be “quick and dirty.” You need a good methodology and a clear understanding of what you’re doing. Determining accuracy of probabilistic tools is best done with extremely large sample data sets and by looking at the smallest data elements in those sets for a yes or no (1 or 0) match. This is much, much more than any one prospective buyer has the time or resources to do at the purchase phase.

I would, however, like to clarify a couple Matching Technology vendors you identify. Resume Mirror is a licensed reseller of Engenium. Engenium is on your list already. And Sovren wraps up DT Search’s matching technology under its own label. DT Search is not on your list. It should be.

@Jeremy – nice analogy. Although part of my point is that semantic search engines the lack intrinsic precision of “hand made” searches, and hammers aren’t really known as a precision instrument. But I like your take on the mass production vs. hand-crafted/high quality angle.

@Jason – I firmly believe that it is critical to understand a process manually before seeking to automate it. There’s certainly nothing instrinsically wrong with automation – it just has to be done for the right reasons, in proper support of people making decisions, as you suggest. I’ve had the honor of working with some really good matching software, and we have a resume database numbering in the millions, and IMO it’s sloppy. Some good matches is not enough for me. That’s like implementing robotics on an assembly line and being able to crank your output, but with a higher defect rate. Although matching to some extent is based on perception, ultimately there is a true match between what the client/manager wants and specific candidates, and software can’t match that, IMO.

Secondly, matching is one persons view of what is right, its not your view, and the ‘your’ needs to be built into candidate selection technology – move away from a pure science to an ‘adaptive’ science – at the end of the day, candidate sourcing technology should do one thing – get a recruiter to a list of qualified and willing candidates who they want to interview – and do it fast.

The technology has to be the enabler in this equation, the recruiter the knowledge – couple adaptive selection with candidate volume and I think you would be in recruiter paradise.

Seems to me that Semantic + Bots = Nail Gun VS. old-school = reg hammer, BUT some of the best furniture/homes are “hand built”.

Hmmmm

parsing:
Textkernel: hhtp://www.textkernel.nl
DaXtra: http://www.daxtra.com

matching:
WCC: http://www.wcc-group.com

matching&parsing:
Lingway: http://www.lingway.com