Semantic Search for Recruiters: Manual vs. Automated

Posted at April 1, 2009

Semantic search has been a hot topic in Internet search for a number of years now and it continues to generate quite the buzz. For example, Google just recently rolled out semantic search capability. However, when I talk about semantic search, I’m not referring to the semantic web or “web 3.0.”

I’m not so excited about whether or not Google can correctly disambiguate my search on the word “bark” and figure out if I mean “the sound of a dog,” “the skin of a tree,” or “a three-masted sailing ship.” I’m interested in an passionate about semantic search techniques and applications specifically geared towards enabling more effective and efficient talent identification and acquisition.

So exactly what is semantic search?

That’s an excellent question! If you run a search on Google or even a semantic search engine such as Hakia or Powerset for the phrase “semantic search,” you’ll find many confusing results and very little in the way of an easy-to-understand explanation of the concept. I’ll attempt to do my best to explain semantic search in a way that is pertinent to sourcing and recruiting.

Quite simply, semantic search can be defined as search techniques and/or applications that attempt to return results that more closely match the “meaning” or intent of the search rather than simply returning results that match the words of the search. “Semantics” refers to the study of meaning, as inherent at the levels of words, phrases, and sentences.

What You Want vs. What You Say

Most sourcers and recruiters search information systems with queries that simply return a collection of words – words that do not have any associative meaning and that are not guaranteed to return relevant results. Relevance can be defined as the extent to which a search result matches the information need of the person executing the search. In other words, relevant results are what you want – highly relevant results are search results that match exactly what the searcher is looking for.

It’s safe to assume that sourcers and recruiters who are searching information systems to identify candidates are actually trying to find people that have specific skills, qualifications, abilities, and experience. However, just because certain words appear in a person’s resume or profile – it does not mean that the person has been primarily responsible for working with those words (typically skills, technologies, etc.). It’s important to be able to create Boolean search strings that return results that you actually want – not just the words you used in the search.

Lexical vs. Semantic Similarity

There is often a critical difference between the semantic similarity between a search and its results vs. the lexical similarity between a search and its results. When search results simply match the search terms but not the intended meaning of the search, there only a lexical similarity (the words match) between the search and its results. When the search results match the intended MEANING of the search, there is a semantic similarity between the search and its results.

Sourcers and recruiters leveraging semantic search are seeking to increase the probability that they will return search results of people who can actually do the job they are looking to hire for – a strong semantic match – not just people who happen to mention the words of the search somewhere in their resume or profile (lexical matches only, aka – false positives).

Automated Semantic Search

When I refer to “automated semantic search” for sourcing and recruiting, I’m referring to applications and systems that claim to perform semantic search for you. Semantic search applications and engines take your search terms and attempt to “understand” the intent of your search (i.e., what you’re really looking for) and return relevant results.

For example – various semantic search applications claim to:

Have “pseudo-Artificial Intelligence” – performing concept matching based on a back end list of keywords (e.g., CPA is associated with accounting, J2EE is associated with Java, etc.)
Execute “context-aware” semantic matching of job descriptions and resumes
Perform fuzzy search/matching – returning results that are likely to be relevant
Have “full Artificial Intelligence” – the software is designed with algorithms to create relationships between words, abbreviations and phrases dynamically and without human intervention

Partial List of Semantic Vendors (with links):

Limitations of Automated Semantic Search

While vendors of semantic search solutions are happy to sell you on the idea that their applications can do all of the work for you in quickly identifying highly qualified candidates, you need to be aware that there are several limitations of automated semantic search.

Semantic search applications that come with pre-programmed lists of “relevant” keywords may in fact return results that are not relevant to your search. Also, a back-end list of keywords can get outdated quickly in some industries.

Fuzzy matching by it’s very definition is “approximate” or “inexact” matching. Does that sound like a good thing? In other words, fuzzy search can return results that are *likely* to be relevant to a search argument even when the search does not exactly ask for it. But who determines the likelihood? Not you.

While fuzzy search/match can help with misspellings and researching unfamiliar terms, I have found it to have limited usefulness for those who know *exactly* what they are searching for.

Many semantic search solutions employing “artificial intelligence” often return results that mention words RELATED to the terms you were searching for – which are words OTHER than the words you actually searched for. While that can sound like a good thing, I have found that in many cases these words that the application “thinks” are relevant are in fact NOT relevant to your specific search. This can actually produce more false positive results that do not match the intent of your queries, wasting your time instead of increasing your productivity.

(Wo)Man vs. Machine – Human Cognition vs. Artificial Intelligence

You must take a moment to reflect and realize that an application that claims to perform semantic search is essentially trying to *guess* the intent of your query – it’s taking your search terms, example resume, or job description, and guessing what might be relevant to you. Never lose sight of the fact that the operative word in “artificial intelligence” is “artificial” – these applications have no true interpretive/cognitive power.

When you are conducting a search for candidates, only YOU can determine what results are relevant to you – or in other words – what kinds of candidates actually are capable of performing the role you are hiring for. As we all know, resumes and social media profiles are often poor and inaccurate representations of candidate’s abilities and experience. Talented and experienced sourcers and recruiters go WAY beyond buzzword matching and quickly and intuitively apply significant interpretive analysis when reviewing search results, effectively “reading between the lines” to identify talent.

As such, I recommend that the results produced by automated semantic search and “artificial intelligence” matching applications be used for “suggested reading” – kind of like when you are searching for something to buy on Amazon.com and Amazon.com suggests products “you might also like.” But please don’t count on semantic search applications to replace talented sourcers and recruiters. I have found that these solutions tend to work best with title matching and searching for “cookie cutter” roles. However, it must be said that even the most junior sourcer or recruiter can run a search for titles and find candidates.

A Final Word of Caution Regarding Automated Semantic Search

You must always keep technology in perspective. I’d like to quote a passage from The Toyota Way Fieldbook by Jeffrey K. Liker and David Meier:

“It is not enough to show in the abstract that IT can automate a process or provide more or better information. It must be clear how it will add value and support a well-thought-out and time-tested process. Typically, the process is done well manually before it is automated. The technology supports human decision making-it does not replace it. And the technology should not be used as an excuse to stop thinking and lose focus on kaizen.”

I could not have said it better myself! Semantic search engines and applications should not be seen as solutions to replace or eliminate human thought, analysis and decision making – they should be used to support human decision making.

What I see as EXTREMELY DANGEROUS is sourcing and recruiting organizations who are excited to implement semantic search/artificial intelligence matching solutions expecting them to “solve the problem” of the intrinsic challenges associated with leveraging information systems and human capital data for talent identification.

If you don’t have anyone on your team or in your organization who is highly proficient in MANUAL semantic search, let alone “standard” Boolean search/Talent Mining, how will you be able to assess if the automated solution is actually doing what the vendor is claiming it can do? How will you be able to test and determine if the automated solution is actually finding all of the available candidates, let alone the best available?

YOU CAN’T!

It is critical that you and your team first master the processes and best practices of manually searching and analyzing human capital data before you fast-forward and attempt to automate a process you don’t understand in the first place. If my word of caution isn’t enough for you, take Toyota’s advice – they’ve proven that they know a thing or two about technology and process automation.

Manual Semantic Search

While the semantic web and semantic search engines employing artificial intelligence/concept matching get a lot of buzz in the HR, recruiting and staffing industry, the big secret is that you don’t have to rely on a search engine, system or an application to perform semantic search for you.

Yes – sourcers and recruiters can perform semantic search manually. I’ve referred to this concept as user-generated/user-defined semantic search in previous posts. Instead of letting an application take an artificially educated guess as to the intent of your search and give you results it *thinks* are relevant to you, you can create Boolean search strings that go beyond simply trying to match the words themselves and attempt to delve into the meaning implied by the words – targeting candidates based on what they DO, not just what they SAY.

Examples of User-Defined Semantic Search

#1 BASIC: adding functional/responsibility-related terms to your searches

admin*, config*, deploy*, manag*, implement*, audit*, reconcil*

#2 MID-LEVEL: NEAR operator (Monster, Exalead…)

(implement* or deploy*) NEAR SAP
reconcil* NEAR (accounts or statements)

#3 ADVANCED: configurable proximity and variable term weighting (Exalead, Lucene, dtSearch)

Exalead: (implement* or deploy*) NEAR/5 SAP

If the NEAR operator and configurable proximity searching are new concepts to you, or you’d like to learn more about how to achieve manual semantic search, I recommend that you read these 5 posts (click on links):

Benefits of Semantic Search

Quite simply, the reason why semantic search can be such a big deal to sourcers and recruiters is that when executed properly, it can:

#1 Significantly reduce sourcing time by reducing/eliminating “false positive” results of candidates who are not likely to be qualified

#2 Increase a sourcer’s/recruiter’s ability to quickly find more appropriately qualified candidates

#3 Enable sourcers and recruiters to move beyond “buzzword bingo” and identify talent based on what they are capable of DOING, not just the words they SAY in their resume/profile

Conclusion

Semantic search IS a big deal for sourcers and recruiters. Every day, more information about more people becomes available in the form of human capital data that can be found in resumes, social media profiles, Tweets, blog posts, press releases, etc. The end goal of leveraging information systems for talent identification (fancy speak for searching for candidates) is to quickly find the RIGHT people – people who are capable of performing the role you are sourcing/recruiting for.

One effective way of doing this is through a combination of manual and automated semantic search – leveraging search techniques and applications that increase your ability to find people who are more likely to meet or exceed your hiring requirements by tapping into the power of the meaning inherent at the levels of words, phrases, and sentences.

Be sure to fully understand and master the concepts and best practices of manual semantic search before jumping to automate a process you don’t fully understand and cannot evaluate properly or effectively. While it can be tempting to try and skip the challenges associated with Talent Mining via Boolean search, trying to replace or eliminate human decision making in the process of talent identification and acquisition would be a HUGE mistake!