Here is the expanded slide deck from my SourceCon 2010 Keynote: Resume Sourcing and Matching – Artificial Intelligence vs. Human Cognition. It contains all of the talking points as text so you are not left guessing as to what I spoke to during the live presentation.

You’ll learn about the intrinsic and often overlooked challenges associated with sourcing resumes (it’s deceptively complex), what artificially intelligent semantic search and match applications claim to do and how they actually work, the limits of artificial intelligence, what people can do that semantic search applications cannot, the 5 levels of semantic search,  the 5 levels of talent mining, and what I think is the ideal candidate sourcing solution.

There are many software vendors who offer solutions to HR, recruiting, and sourcing organizations that claim to have automated candidate search and match capability. These applications can take your search, a job description, or an example resume and claim to leverage semantic search, fuzzy logic and/or Artificial Intelligence search technology to return relevant results. 

I’ve had the opportunity to use and evaluate 4 “big name” semantic and intelligent search/match applications for identifying candidates, and I am currently implementing one of them, including cutomizing the taxonomies and ontologies - so I have quite a bit of hands-on, practical experience with semantic search applications. While I do think that intelligent semantic search and match applications definitely have a place in the sourcing and recruiting process, they should not be looked at as solutions to a problem, challenge, or a deficiency in your skills or your team’s.

Before implementing a semantic search application, it is important to first understand the manual search process, how automated semantic search works, and the intrinsic limitations of of semantic search applications. 

People Do the Work, Computers Move Information

Eiji Toyoda, the fifth president of Toyota Motor Corporation, who collaborated Taiichi Ohno to fine tune the concept of Kaizen as well as to develop the core concepts of the ‘Toyota Way’, explains brilliantly: ”Society has reached the point where one can push a button and be immediately deluged with…information. This is all very convenient, of course, but if one is not careful there is a danger of losing the ability to think. We must remember that in the end it is the individual human being who must solve the problems.” 

As I have written before, thinking is the most critical step in the candidate sourcing process, and regardless of “Artificial Intelligence” and semantic search marketing hype, applications do not have any true cognitive power, nor do they have the ability to be creative or learn as people do. Thus I could not agree more with Jeffrey Liker’s (author of The Toyota Way) assessment - ”People do the work, computers move information.”

Do Not Automate What You Cannot Already Perform Manually

If you are not a highly proficient Talent Miner, capable of manually querying databases and systems with Boolean logic and a high degree of precision to find not only the obvious and easy to find candidates, but also the those residing in Hidden Talent Pools – how can you even HOPE to begin to evaluate what an automated semantic search solution is claiming to do?

You can’t.

If you’re going to buy and use product or service of ANY kind, and you don’t really understand what it does or even exactly how it does it (beyond the marketing hype), and you can’t tell if it REALLY does everything it claims to – how and at what level can you determine if the product or service truly meets your needs and will provide true value?  

If you or your organization struggles with the challenge of finding the right candidates at the right time on a consistent basis, implementing an automated search and match application will not magically solve this “problem” for you. The real underlying problem is likely that you or your organization currently does not #1 possess the right skills or the right people who are highly proficient at candidate sourcing and/or #2 have documented highly effective candidate sourcing processes and best practices that are consistently trained to and followed. No software application can fix either or both of those issues.

Jane Beseda, Group VP at Toyota, believes that it is best to “First work out the manual process, and then automate it. Try to build into the system as much flexibility as you possibly can so you can continue to kaizen the process as your business changes” because “…you can kaizen (continually improve) people processes very easily, but it is hard to kaizen a machine (or application).” I agree wholeheartedly.

Master the Sourcing Process Manually, THEN Introduce Automation

If you or the people on your team don’t fully understand how to effectively leveraging technology for talent identification and you can’t perform it manually, I do not recommend implementing an automated search/match solution. It is critical to #1 First develop your skills and ability (or your team’s) to manually source candidates, #2 Document your sourcing best practices and processes, #3 Make sure that they are consistently trained and applied, and #4 Strive to continually improve them. THEN you can go about evaluating automated search and match solutions because you will actually have the ability to truly assess and understand what the products do, you will be able to determine whether or not they meet your needs and can provide real value to your organization, and you will be able to assess how you can possibly best leverage them into your sourcing efforts.

For example, if you don’t really comprehend the concept of semantic search and how it can be applied in candidate sourcing (e.g., you can manually leverage semantic search in your Boolean strings at an expert level or you have someone on your team that can), you won’t be able to tell whether or not a application claiming to leverage semantic search is really effectively doing so, if it will provide any real value to your sourcing efforts, or if it is finding all of the best possible matches in the sources it is searching.

Speaking of Semantic Search

Many automated candidate search and match solutions claim to leverage semantic search. I’ve written many articles on the topic of how semantics can be leveraged for sourcing candidates – so I won’t go into too much detail here – but semantics refers to the study of meaning, as inherent at the levels of words, phrases, and sentences.

Sourcers and recruiters can leverage semantics in their sourcing efforts to more quickly find more relevant results. When the search results match the intended MEANING of the search (the INTENT), there is a semantic similarity between the INTENT of search and its results. In other words – you get what you’re looking for. When search results simply match the search terms but not the INTENT of the search, there only a lexical similarity between the search and its results. In other words – the words match, but you don’t get what you’re looking for.

For example, if I were manually searching for Linux systems administrators – that is the INTENT of my search – to find people who have been primarily responsible for administering Linux systems, regardless of exactly how they express that experience in their resume. However, with basic keyword and title search, I will find a mix of relevant results (people who have been responsible for Linux administration) and irrelevant lexical-only match results (people who happen to mention the search terms of Linux and systems and administraion somwehere in their resumes or profiles, but who have not been primarily responsible for Linux systems administration).

However, as a thinking human being, I can truly learn from the search results and continually improve and adapt my search strategies and keywords to leverage the language used specifically by people who have been primarily responsible for administering Linux systems (using semantics – the actual meaning of specific words and phrases), ensuring more of the results returned actually match the INTENT of my search.

So How Do Applications Achieve Semantic Search?

To provide relevant search results, various automated search and match applications claim to:

• Have “pseudo-AI” – these perform semantic “concept matching” based on a taxonomy/ontology (lists of terms, equivalent terms and their relationships)
• Perform “natural language” search – using pre-programmed phrases and/or proximity matching
• Execute “context-aware” semantic matching of jobs and resumes
• Perform fuzzy matching – returning results matching words that were not specifically searched for by the user, but that the application “thinks” is likely to be relevant
• Have “full AI” – the software is designed with algorithms to create relationships between words, abbreviations and phrases dynamically and without human intervention

Applications Are Not Mind Readers

No software application is capable of determining the INTENT of any search – it is critical to recognize and understand this, because it’s at the very core of semantic matching, although it’s not often written about when it comes to sourcing candidates. No application can actually *know* what you’re looking for – only YOU know what you’re trying to find, which is a person who has had some sort of specific experience performing certain responsibilities, often in specific environments.

Essentially, automated search/match applications take either your search terms, a job description, and/or an example resume and make their “best guess” as you your intent. Don’t get me wrong – some applications do a very good job of taking what you give them and providing some relevant matches. However, you must always be aware that these are just that – guesses – and on top of it all, they’re guesses made by an application, not a person.

Output is Limited by Input

Search/match aplications are only as good as their input - if a user is not especially adept at crafting search strings the quality of the results the application can produce will be limited. Additionally, we are all aware the resumes and job descriptions are FAR from the best representations of skills and experience, so the results produced by an application interpreting a resume or a job order is instrinsically limited.

There is no replacement for the cognitive and interpretive power of the human mind – people are much more capable of “reading between the lines” of resumes and job descriptions and getting to the true essence of the required skills and experience to determining how to best approach sourcing efforts to find the right people. Applications can claim to do this, but it’s an apples to oranges comparison.

Let’s also remember that a sourcer or recruiter can discuss the position’s requirements with the hiring manager (in most cases – if not, they should be able to!) – this information is typically beyond what any example resume or written job description can (or does) convey. Armed with this critical information and detail, a sourcer or recruiter can translate what they’ve gathered verbally from the hiring manager/team into a sourcing strategy, down to the Boolean search string level. The last time I checked, an application cannot do this. There is no replacement for good analytical and interpretive ability – it’s precisely why you can’t fully automate a business analyst’s, a data analyst’s or a financial analyst’s job.

Part of the instrinsic challenge faced by automated search and matching applications is that they are expected to make the leap from “give me what I said” (match the words) to “give me what I want” – matching your intent. Considering that an application is incapable of “understanding” your intent, how can it hope to deliver results that match your intent when your intent cannot be interpreted from an example resume, a job description, or a poorly defined search? 

Intrinsic Limitations of Automated Semantic Search/AI

Be aware that any automated semantic search/match application is only as good as its programming, taxonomy, and ontology:

• Pre-programmed lists of “relevant” or related keywords may in fact not be relevant or related to your specific search, and can get outdated quickly
• Fuzzy matching is by definition “approximate” or “inexact” matching, the very opposite of “precise”
• Applications are essentially “guessing” the intent of your search based on the keywords, resume, or job description you feed it – but finding the best talent is not a guessing game
• Some systems will return results with related words – which may in fact NOT be relevant to your specific need – will you be able to determine which are and which aren’t?
• Applications that claim to “learn” may not actually improve the relevance of results over time

The Tough Questions

Are you capable of evaluating an application “under the hood?”  When a vendor tells you that their application is “trained to identify the equivalent meanings of terms found in resumes,” do you know how to get to the bottom of exactly how their application accomplishes this, and whether or not their application can actually do it for your specific hiring profiles? Could you manually run searches to objectively evaluate the vendor’s claims? If you don’t already possess the ability to manually source candidates from information systems, what will you do when your automated search/match application fails to produce the right results, or enough of them? Do you have the ability to search your own database to find the candidates that the search/match application can not find/misses? 

My Experience and Opinion

I’ve had the opportunity to use a number of automated semantic search/match applications. I’m also currently implementing one and I’m involved in customizing the taxonomies and ontologies. I’m very excited about the features and capability of the semantic search application I’m using. However - I’ve found it does have it’s limitations, and that there is no perfect solution that magically produces the best candidates available with the push of a button, nor can an automated intelligent/semantic search application fully replace a skilled sourcer or recruiter. Augment and empower - yes. Replace – no.

As someone who is highly proficient in manual Boolean and semantic search, from everything I have gathered, most automated search and match applications simply automate basic sourcing best practices, but none that I have used do so completely or flawlessly.

In fact, some search/match apps appear to do little more than heavily leverage common title and skill terminology matching – which any sourcer or recruiter of average skill can accomplish. Other applications go overboard and get sloppy in the process, suggesting and incorporating alternate search terms it *thinks* are related and relevant to your search – and in some cases the terms are actually NOT relevant to the intent of the user’s search, causing more harm than good. But if you don’t know what to look for (past the marketing and past the huge cloud of “related” search terms the app suggests) and you don’t know how to get to the core of the search logic and the true relevance based on the intent of your search, you might just actually believe you have the solution to all of your candidate sourcing problems.

While some industries can benefit heavily from automated search and match applications – for example,  those with simple and highly consistent titles, lexicology, and phraseology(such as finance and accounting), others vary widely and change rapidly (such as Information Technology) and pose serious challenges for vendors of automated search and match solutions.

Check Your Reasoning

If you’re looking to implement an automated candidate search/match application, simply ask yourself why – and dig down to the very root of it. Is it because Boolean searching is “too hard?” Is it because you think the application can reduce your costs by reducing headcount? Do you think a search/match app can speed up the talent identification cycle?

I’ll let you in on a little secret – Boolean search isn’t that hard with the proper training. It’s more science than art, really. Which is good news, because manual talent mining via Boolean search strings can be broken down to repeatable steps, including the interpretive and analytical processes, which can be continually and dynamically improved – whereas an application cannot (easily, or at all). 

Also – semantic search/match applications are not a replacement for people, because the applications don’t actually perform real work or deliver value to the end customer. When I refer to “work” and “value,” I’m referring to how Lean and the Toyota Production System define those terms. Recall Jeffrey Liker’s assessment, “People do the work, computers move the information.” The author of The Toyota Way has also explained that ”…the only thing that adds value in any type of process…is the physical or information transformation of that product, service, or activity into something the customer wants.”

By customer, Jeffrey means the END customer – the internal hiring manager or the external client. Automated search/match apps don’t transform or produce a product that the end client wants – which is a talented person who is a great match for their need. Automated search/match applications produce resumes and profiles (i.e. “computers move the information”) and the sourcers and/or recruiters analyze and transform those resumes/profiles into fully screened, closed, and qualified candidates for the end customer (i.e., people do the work). 

My Suggestions

Automated search/match applications definitely have their place in world of sourcing and recruiting. I think they are best used to facilitate and augment the talent identification efforts of sourcers and recruiters. Some can be especially useful in quickly and simultaneously searching multiple online sources of resumes and social media profiles and parsing large volumes of results into your internal ATS/CRM – this quick and permanent human capital data capture can be a major benefit of using some search/match solutions.

Ultimately, I feel that sourcers and recruiters using AI/semantic search applications should utilize the results they produce and return as ”suggested reading” - but I would never rely solely on an application to exhaustively identify top talent, just as no one would trust a plane full of people to take off and land on autopilot. 

Whether or not you are considering purchasing an automated search and match application or service, or you’ve already implemented one, I strongly urge you to #1 First develop your skills and ability (or your team’s) to manually source candidates, #2 Document your sourcing best practices and processes, #3 Make sure that they are consistently trained and applied, and #4 Strive to continually improve them. THEN asses the best way to leverage an automated matching solution to augment your talent identification efforts.

Do not take a poorly functioning sourcing process or team and expect to fix it using an automated search/match application. Fine tune your sourcing process and best practices, develop your sourcers and recruiters with exceptional training, and then surgically insert matching technology to enhace them. Technology is a tool that exists to support your people and your processes – it is not a solution to a problem nor a replacement for a process or a person. 

Conclusion

I am in no way against inserting semantic search applications into the sourcing function (remember, I’m using one now!) – but I feel it must be done for the right reasons AND with a full understanding and capability of performing the sourcing processes manually, else you cannot continually improve your sourcing processes, nor will you be capable of picking up sourcing where the application fails to deliver.  

If finding some candidates is your end goal, then you can feel comfortable using automated search and match solutions to do all of the “heavy lifting” for you or your associates when it comes to talent identification. However, if you goal is to find all of the best candidates, then you should use an automated matching application as a tool to support your sourcers and recruiters who are well trained and effective in manual sourcing best practices.

Caveat Emptor

If you are looking to purchase an automated candidate search/match solution, or you’ve already implemented one, and you do not have the expertise or experience associated with assessing and implementing such solutions – I STRONGLY urge you to seek a neutral, third party HR/Recruiting technology consultant and involve them in the process. It is all too easy to be sold by a vendor’s marketing and messaging – but you are at a distinct disadvantage if the vendor knows more than you about the sourcing and matching function – be it manual OR automated.

Be wary – do not seek to automate a process that which you do not fully understand how to perform manually.

Partial List of Vendors of Matching Technology:

• Pure Discovery: http://www.purediscovery.com/
• Actonomy: http://www.actonomy.com/
• Semetric (Engenium): http://www.krollontrack.com/semetric/
• TalentSpring: http://www.talentspring.com/
• Sovren: http://www.sovren.com/
• BurningGlass: http://www.burning-glass.com/
• ResumeMirror: http://www.talenttech.com/

Are you a vendor and want to be added to this list? Would you like me to evaluate your product? Let me know.  Thanks!

It’s been a long time coming, but I have finally gotten around to creating a resources page that essentially contains a “best of” compilation of Boolean Black Belt articles. It contains 10 “How-To” posts ranging from how to search Linkedin, Twitter, Facebook, and Google for candidates, as well as articles on semantic search, Boolean, extended Boolean, and the top 15 common e-sourcing mistakes.

Here’s where to find it:

Enjoy!

I define E-sourcing as leveraging information systems for active talent identification – searching the Internet, social media, job board resume databases, and applicant tracking systems to find candidates. The proper use of technology in the sourcing and recruiting process should increase your efficiency, productivity, and effectiveness.  I’ve created the SlideShare presentation below to cover a number of different ways for you to maximize your ability to find more of the right people more quickly, to accelerate and enable your recruiting efforts.

Click on the presentation below to review:

• Boolean operators and common query modifiers
• Searching LinkedIn, Twitter, and Facebook
• X-Ray searching Social Media
• Search automation and aggregation
• Semantic search: manual and artificial intelligence matching solutions
• Search ROI – a comparison of the searchability and data depth of the Internet, Social Media, Resume Databases, and ATSs
• Talent Warehouse concepts

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):

• Pure Discovery
• Actonomy
• Semetric (Engenium)
• TalentSpring
• Sovren
• BurningGlass
• ResumeMirror
• Autonomy

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):

• Semantic Search for Sourcers and Recruiters
• Semantic Search for Sourcers and Recruiters Round 2
• Semantic Search Using the NEAR Boolean Operator
• Achieving Semantic Search Without Proximity Operators
• Extended Boolean: Proximity and Weighting

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!

How sourcers and recruiters can achieve semantic search WITHOUT using Boolean proximity search operators such as NEAR

If you’ve read these 3 posts: Semantic Search for Sourcers and Recruiters, Semantic Search for Sourcers and Recruiters Round 2, and Semantic Search using the NEAR Operator, you already know I am a fan of leveraging semantic search for sourcing and recruiting. I believe that it is critical to go beyond basic buzzword matching when creating Boolean search strings and attempt to tap into semantics – searching for words in specific contexts to find people based on what they DO, not just the buzzwords they happen to mention in their blog posts, social media profiles, or resumes. 

So far, I’ve only discussed how to achieve semantic search using Boolean proximity operators, such as NEAR.

True user-defined semantic search is best achieved though proximity operators such as NEAR and w/x mainly because of the ability to control sentence structure to search for specific responsibilities and experience rather than keywords alone.

However, although everyone has access to an Internet search engine that does support the Boolean NEAR proximity operator (Exalead), not everyone has access to Applicant Tracking Systems such as Bullhorn or online job board resume databases like Monster that support proximity searching, so I am dedicating this post to explaining how sourcers and recruiters can achieve semantic search without using any proximity operators.

Traditional Keyword Search

When most sourcers and recruiters create Boolean search strings, they tend to focus solely on titles, skillsets, and technology buzzwords, like accountant, software engineer, SAP, project manager, FASB, .Net, Java, SQL, etc.

However, while this is a commonly accepted practice, searching for words like those returns many false positive results by design. A false positive result is a result that matches the search terms, but does not meet the information needs of the user.  In other words, a false positive is not a relevant result.

If you’re searching the Internet, creating Boolean search strings looking only for technologies, skillsets and/or titles doesn’t guarantee anything other than results that mention those words – not necessarily PEOPLE, which if you are a sourcer or a recruiter I’m assuming you’re targeting. 

If you’re searching LinkedIn, your internal resume database/ATS, or an online job board resume database and you get results of people who mention those titles and/or keywords you’re searching for – you’re certainly not guaranteed that those people actually have experience doing what you need them to.

Just because someone mentions Java, J2EE, and Weblogic in their blog, profile, or resume, it does not mean that they have the required experience with those technologies that you need. The same applies for results that mention “accountant,” ”project manager,” or any other title or technical term.

Sometimes the results you get from “traditional” keyword searching are of people who mention the terms you’re looking for, but you can’t tell if they are remotely qualified for your needs – so you can contact them to find out if they do or not, and at least network with them if in fact they don’t.  Other times the results you get from “traditional” keyword searching are pure junk – results that mention the words you’re looking for but the person is obviously not qualified for anything you might be looking for now, or in the future.

While most sourcers and recruiters accept having to slog through false positives and irrelevant results as inevitable and “just they way it is,” I prefer to ask if there is anything we can do about it.

Semantic Search

A more effective way to leverage Boolean search strings for talent indentification is to include functional, or responsibility-related terms in addition to titles and skills. Functional terms can help you target and highlight what the people you’re targeting actually DO, giving you insight into their experience, capability and level of responsibility – which is what your hiring manager really cares about. Hiring managers don’t (or shouldn’t!!!) care about titles or buzzwords – what’s really going to come out on the interview is whether or not the candidate has been responsible for the types of things that the manager needs them to have been responsible for in order to perform the job they are considering hiring them for. Right?

Here are a few examples of functional/repsonsibility-related terms you could add to your Boolean search strings in an attempt to search beyond basic skill, title, and technology keywords: 

Administer, design, develop, implement, integrate, configure, manage, reconcile, audit, schedule, etc.

Remember that Google auto-stems every search term you enter unless you put it in quotes or precede the search term with a +, so adding any one of those words, as appropriate, should net you all of the word variants. You could also try adding the tilde (~) to functional terms and it will look for synonyms. For example: ~Administer, ~design, ~develop, etc. 

On Exalead and the major job boards, you can leverage the asterisk for root word/stemming. For example: Admin*, design*, develop*, etc.

Coupling appropriate functional/responsibility search terms with traditional title and/or technology terms to attempt to perform imprecise semantic search. I say “imprecise” because without using proximity search operators, you cannot precisely control whether or not someone mentions Java close to the words developing, developed, developer, develop, design, etc. If responsibility terms are not mentioned close to technical/skill terms, the probability isn’t as high that there is any semantic relation between the two. We won’t be able to eliminate the false positive results of people who simply mention our search terms somewhere, but don’t have enough of, any, or the right type of experience. 

However, adding functional/responsibility-related search terms does have several benefits:

• Many Internet search engines automatically favor search terms that are mentioned in close proximity within results, so some search engines may return and rank highly results that do in fact happen to mention functional/responsibility related terms close to technology/skill terms and successfully yield results that have a high semantic similarity to the intent of the search
• Simply adding more search terms such as functional/responsibility-related words to your Boolean search strings gives search engines more words to search for and determine ranking and relevance. Results will have their relevance ranked by responsibility related terms in addition to standard buzzwords such as titles and technologies.
• With functional/responsibility-related terms added to your Boolean search strings, simply having those terms in the results allows you to scan the results quickly to not just look for buzzwords, but also for words describing what the people DO and have DONE.

Search Examples:

On Google:
(intitle:resume | inurl:resume) Java J2EE (SOA | SOAP | service) (~design | ~develop) -~job -~jobs

View the results

(intitle:resume | inurl:resume) UNIX (”system” | “systems”) ~administer Linux “Red Hat” (”server” | “servers”)  ~design -~job -~jobs

View the results

(intitle:resume | inurl:resume) “accountant” reconcile (bank | financial) statement -~job -~jobs -sample

View the results

On resume databases and ATS’s support the asterisk for stemming:
java AND J2EE AND (SOA* OR servic*) AND (design* OR develop*)

UNIX AND system* AND admin* AND Linux AND “Red Hat” AND server* AND design*

“Accountant” AND reconcil* AND (bank* OR financial) AND statement*

Conclusion

While most sourcers and recruiters simply accept the fact that it’s “normal” to get a large false positive/irrelevant results from Boolean search strings, there are many things you can do to decrease false positive and increase the relevance of your results.

Throwing in titles, skillset and technology terms into your Boolean search strings is like playing “buzzword bingo” - results are only required to have the words you searched for in them, and just because a document mentions a specific title, Java, Oracle, SQL, UNIX, FASB, SOX, PHP, or any title/skillset/technology term that you might be looking for, it doesn’t MEAN anything other than the words are present.

Semantics is the study of meaning. The presence of search terms like Java or SOX in a document do not have any intrinsic meaning in and of themselves – most meaning comes from context. Now, if those words are mention in the context of other words, say – being responsible for doing specific things with them (e.g., designing portal applications in Java, or performing SOX audits), there IS meaning.

If you consciously decide to add functional/responsibility-related words to your searches (such as perform or design), in conjunction with titles, skillset and tecnhology terms, you increase the probability that you can, by design, return results that are more relevant to you – results of people who don’t just mention the search terms, but have been responsible for DOING the kinds of things you need them to have experience with.

Even if you are focused soley on name generation, and certainly if you are sourcing and/or recruiting for specific positions/hiring profiles, you ARE ultimately looking for people with specific experience/capability. Leverage semantics and use functional/responsibility related terms in your Boolean search strings in conjunction with more traditional keywords to increase the relevance of your searches and find the people that have the experience you need.

By the way – if you can tweak your searches and find more of the right people more quickly – you’ve just increased your productivity. But that’s a whole ‘nother post.

This post will cover graphic examples of how to achieve semantic search using the NEAR Boolean operator on Monster and on the Internet via Exalead using Accounting and Information Technology hiring profiles.

First, if you have not done so already, be sure to read these 2 posts that throroughly explain the concepts of user-defined semantic search for sourcing and recruiting: Semantic Search for Sourcers and Recruiters, and Semantic Search for Sourcers and Recruiters Round 2. 

Second, if you’re not already familiar with the NEAR operator, I highly recommend you read about it in this post: Extended Boolean – Proximity and Weighting (look for it in the middle of the post under “Fixed Proximity”) before proceeding any further.

The NEAR operator on Monster

Monster is the only major job board resume database that recognizes and supports the NEAR Boolean operator. According to Monster’s documentation, the NEAR operator has a maximum proximity of 10 words. For example, the search string software NEAR programmer returns ONLY those resumes that have software and programmer within 10 words of each other. 

Let’s look at a couple of resume snippets that I used from Monster when I wrote a post on Boolean Search Strings for a Sales Tax Manager. What we’re going to do is go beyond the individual words themselves and look for how the candidates wrote sentences describing specifically what they’ve been responsible for doing. Then we will use the NEAR operator to create Boolean search strings that go beyond simply trying to match the words themselves and attempting to delve into the meaning implied by the words by targeting sentences describing responsibilities.

Notice in both resume snippets, we see verbs like prepared, supervised, managed in close proximity to words like tax, statements, returns, workpapers, schedules, month and quarter end, personnel, staff, etc.

Let’s use the NEAR operator to specifically target sentences where candidates are talking about the types of responsibilites we need them to have had experience with:

prepar* NEAR (tax* or statement* or return*) and (manag* or supervis*) NEAR (personnel or accountants or staff) and tax NEAR (manager or supervisor) and (state or local or federal) and (sales or use)

Breaking down that search – here is exactly what’s going on with the use of the NEAR operators: 

#1 prepar* NEAR (tax* or statement* or return*)

This will require the results to have ANY word starting with the root of tax, statement, or return within 10 words of any word beginning with the root of prepar*

Semantic analysis:
This aspect of the search will be highly likely to return results of resumes that have     sentences mentioning responsibilities such as being responsible for the preparation of tax returns, statements, and returns 

#2 (manag* or supervis*) NEAR (personnel or accountants or staff)

This will require the results to have ANY mention of any word starting with the root of manag or supervis within 10 words of personnel, staff, or accountants

Semantic analysis:
This aspect of the search will be highly likely to return results of resumes that have     sentences mentioning responsibilities such as managing and supervising personnel, staff, or accountants  

#3 tax NEAR (manager or supervisor)

This will require ANY mention of the word tax to be within 10 words of the words manager and/or supervisor

Semantic analysis:
This aspect of the search will be highly likely to return results of resumes that have     titles of tax manager or tax supervisor, as well as resumes with sentences mentioning responsibilities such as managing and supervising tax-related work and/or personnel 

Here are a few examples of the results returned by the above search that clearly demonstrate the NEAR operator hard and effectively at work:

If you’re very observant, you’ll see that the second snippet has a title of “Section Manager – Income Taxes.” The reason I highlight this is that a traditional title search for titles such as “Tax Manager” or “Manager of Tax” could not return that result – a nice example of a candidate that most sourcers and recruiters would not be able to find based on a common title search approach, and proof that hidden talent pools really do exist.

However, using the NEAR operator gives us a handy alternative to specific title searching, and by design, allows us to find candidates with not-as-common titles such as “Section Manager – Income taxes.” Cool.

Now, while the first 50 results of that search had it’s fair share of false positives – such as candidates who are now controllers, CFO’s, and such, but were at some point in their careers responsible for managing tax reporting and personnel, I want to point something out:

Using the NEAR operator, that search pulled 10 direct hits among the first 50 results that had titles of Tax Manager, Supervisor, or Director.

Then, out of curiosity, I took out the NEAR operators and simply replaced them with ANDs:

prepar* and (tax* or statement* or return*) and (manag* or supervis*) and (personnel or accountants or staff) and tax and (manager or supervisor) and (state or local or federal) and (sales or use)

Only 4 out of the first 50 results happened to be direct hits of candidates who had titles of Tax Manager, Supervisor, or Director. So we were able to more than DOUBLE our highly relevant matches among the first 50 results alone by employing the NEAR operator. Sweet.

Now let’s show some love to Information Technology sourcers and recruiters

Let’s say you’re looking for software engineers that, among other things, have deep Java experience as well as specific experience designing portals. This one is easy.

Java and (design* or develop*) and portal* NEAR (design* or develop*)

The double mention of the phrase (design* or develop*) is not redundant.  The first mention is to find any mention of any word starting with the roots design or develop.  The second mention is in conjunction with the NEAR operator, and REQUIRES all results to also have any mention of portal or portals to be within 10 words of any word starting with the root of design or develop.

Semantic analysis:
Using the NEAR operator to ensure that any mention of the words portal or portals is within 10 words of design* or develop* increases the probability that they are mentioned in the same sentence – and if they are mentioned in the same sentence – it’s highly likely that the person is talking about being responsible for designing/developing portals. Which is exactly what we’re looking for.

This is specifically different from just throwing all of the words together and HOPING we get some people who have been responsible for portal design/development. Hope is not a strategy. So we’re using the NEAR operator to target people who ARE responsible for portal design and development, by the design of our Boolean search string.

Here are 3 resume snippets that demonstrate the NEAR operator working its magic:

As you can see, using the NEAR operator worked quite nicely – we targeted the semantics of talking about being responsible for designing/developing portals – and we got it.

The NEAR Operator on Exalead

I realize that not everyone has access to Monster, so let’s take a look at an Internet search engine that everyone DOES have access to – Exalead.

Exalead is the only decent-sized Internet search engine to support proximity searching in the form of the NEAR operator. I say “decent-sized” because it’s not a major search engine in my opinion – mostly because it does not appear to index near as many pages as Google, Yahoo, Live, or Ask, and this is especially and painfully evident when you do back to back searches using the site: command to x-ray into LinkedIn. More on that in another post (here’s a quickie – feel free to run just site:linkedin.com on both Exalead and Google and you will find 10X the total results on Google).

Let’s take a swing at the Java/portal developer search we used above on Monster and point it towards the Internet via Exalead.

(intitle:resume OR inurl:resume) AND java AND (design* OR develop*) NEAR portal* AND NOT job*

Before we look at the results, be aware that unlike Monster, Exalead’s NEAR distance is 16 words – which is getting a little “out there” in terms of proximty.  Also – see how I was able to actually use the asterisk for root word/stemming?  Man that feels good to be able to do that on the Internet. Anyone from Google reading this?

Click here for the results.

Pretty nice results, right? If you check the results, all of them mention portal or portals within 16 words of design*/develop*, in most cases resulting in sentences where the candidate specifically talks about being responsible for/performing portal design/development. Which is exactly what we’re looking for – NOT just people who happen to mention those words somewhere in their resume. We’ve leveraged semantics in our search approach rather than reosrting to the ”buzzword bingo” game.

But wait – there’s more! Exalead also supports configurable proximity. So if 16 words is too far of a gap for you and leads to less relevant results, we can tighen that range.

For example, let’s limit the distance between any mention of design*/develop* and portal* to a maximum of 5 words.

(intitle:resume OR inurl:resume) AND java AND (design* OR develop*) NEAR/5 portal* AND NOT job*

Click here for the results.

We’ve managed to cut the total number of results down significantly, and we’ve also increased the relevance while reducing false positives. Look at how tight those results are! It’s because every single result HAS to mention design*/develop* within 5 words or less of portal*.  We are leveraging semantics heavily here because most mentions of those words in such close proximity are in fact referencing responsibility – we’ve blown past word matching to nail people talking about doing what we need them to have experience with! Am I the only person excited about this?

However, as exciting as this is and as tight as those results are, we have to be cognizant of the fact that there ARE relevant results we just eliminated.  Yup – anyone who mentioned design*/develop* at a range of 6-16 words from portal* was wiped away and we did not see them.  Which is okay, as long as you are aware of this and know how to go back and get them.

As I’ve discussed in many posts, I think the best approach to secondary sourcing is to start tight and highly focused on the most relevant results rather than starting broad and beginning a search buy sifting through tons of false postitives. In other words, if I were target shooting – I would start with a sniper rifle and try to hit the bullseye, rather than start with a shotgun and just be happy to hit the target. 

Applying NEAR to LinkedIn

Let’s use the site: command to use Exalead to x-ray into LinkedIn and exploit Exalead’s configurable proximity search to find Java developers who have been responsible for designing/developing portals.

site:linkedin.com AND java AND (design* OR develop*) NEAR/8 portal* AND (inurl:pub OR inurl:in) -intitle:directory

Click here for the results.

Yup, NEAR works there as well. Every result mentions some mention of design*/develop* within 8 words of portal or portals.  

Also – did you notice how I did not use the -/minus sign coupled with 4 to 6 things I was trying to avoid like jobs, answers, and such? I have found that using (inurl:pub OR inurl:in) is a little cleaner - it simply targets public profiles and all I need to NOT out is intitle:directory in most cases.

Conclusion

Being able to control how close words are mentioned to each other via the NEAR operator enables us to achieve semantic search – tapping into sentence structure and the power of meaning in language. Instead of throwing a bunch of words together and having to sift through large volumes of irrelevant and false positive results, we can attempt to harness semantics to find people based on what they have experience DOING, not just based on what words they happen to include somewhere in their resume.

Kudos to Monster for being the only major online job board to support the NEAR operator, and props to Exalead for not only supporting NEAR, but going a step further and supporting configurable proximity via NEAR/x.

Can’t get enough of semantic search for sourcing and recruiting? My next post will cover how to achieve semantic search for sourcing and recruiting without using any proximity operators. 

Stay tuned!

Looking at my blog stats, it appears I may have struck a nerve when I wrote a post on semantic search for sourcers and recruiters on Monday, December 29th, explaining the concepts of semantic search with regard to how it can be leveraged effectively by sourcers and recruiters.

I received several questions, comments, and inquiries for more information on the subject. If you perform some research on the concept, you likely won’t find much, if anything specifically focused on user-defined semantic search, let alone user-defined semantic search for sourcing and recruiting, so I figured I would take this opportunity to quickly follow up and expand upon the concepts of leveraging semantic search when creating Boolean search strings. 

Irina Shamaeva, of Boolean Strings fame (on both LinkedIn and Recruitingblogs), asked a very insightful question about whether or not I would agree that in fact all sourcers and recruiters are trying to perform semantic search when they are looking for candidates who match a job description

While I would agree that most sourcers and recruiters are trying to find people who have had specific experience performing the role and responsibilities required (and desired) of a job description – I have to say that most sourcers and recruiters are NOT actually performing semantic search. In other words, they are not specifically leveraging semantics in their search tactics and strategies. Throwing a collection of search terms into a Boolean search string based on a job description is just that – a collection of words, which do not necessarily imply any meaning in and of themselves.

Adding additional keywords that may not be explicitly mentioned in a job description, or using the NOT/- operator to eliminate false positive results may (or may not) help narrow search results, and while these are certainly search best practices, they are not instrinsically semantic search. Adding or selectively removing keywords/search terms in many cases simply produces results with the search terms (and without the removed terms) but not implying any responsibility with the search terms.

Using a User Interface Engineering position as an example, there are many people who can mention the words (UI OR user interface OR GUI) in their resume who in fact do not have any significant experience with interface design, even though the words are somewhere in the resume. Even if we added additional UI-related terms such as (wireframe OR human factors OR cognitive OR heuristic) we can still return many resumes of candidates that mention the search terms but who have not been primarily responsible for interface design.

When creating Boolean search strings to find potential candidates – the whole point is to find people who have specific skills and experience performing the role/responsibilities of the position the sourcer/recruiter is looking to fill – in other words, relevant results.  Certainly no one sets out to run a Boolean search to specifically find a bunch of people who aren’t qualified. Any results returned from a search of candidates who do NOT have the skills and experience necessary to perform the responsibilities of the position the sourcer/recruiter is looking to fill are essentially irrelevant results.  Sure, you can build relationships with them and ask for referrals, and perhaps they fit another position, but there is no denying that they simply do not match the intent of the search. 

Sourcers and recruiters encounter this all the time when creating Boolean search strings – the search terms are present in the resume, sometimes in large quantity, but the person has not actually DONE what they need them to have done in their career. That is an excellent example of a high lexical similarity between a search and the results (the words match) and low semantic similarity of the search and the results (the person’s experience does NOT match). 

High lexical similarity and low semantic similarity between a search and its results can also be evidenced in the “technical skills summary” of most resumes, where a laundry list of skills and technologies are present. But just because something is mentioned in a resume, it does not imply any level of expertise, recency of experience, or even paid experience. Hence someone could mention Java, Eclipse, and Swing in their resume, but not have any paid experience developing applications with them (as in educational experience or at home). This is a normal experience for sourcers and recruiters and so they assume this is simply “the way it is.”

However, if we use the NEAR command (or even better – the more powerful configurable proximity search operators of Lucene and dtSearch), we could add this to a search string on Monster: (develop* OR design*) NEAR (Java OR Eclipse or Swing), and the results MUST mention Java or Eclipse or Swing within 10 words of develop or design, increasing the likelihood that the results will include resumes that have sentences specifically stating development or design-level responsibility with Java/Eclipse/Swing. This is tapping into semantics – the mere presence of words in a resume does not necessarily imply any meaning, but words in the same sentence do imply meaning in most (but certainly not all) cases.

The NEAR operator and configurable proximity functionality of search applications such as Lucene and dtSearch are the best ways to leverage semantics when searching because they allow you to target sentence structure, such as when people talk about doing X with Y (configuring routers, reconciling reports, administering a server cluster, implementing SAP, customizing interfaces, performing SOX audits, etc.).

We must also recognize that the candidates that sourcers and recruiters search for are decidedly NOT profesisonal resume writers. However, they don’t have to be. Even though the majority of people are not very good at writing resumes and are clueless as to how sourcers and recruiters search for their resumes and analyze them, most people do create simple sentences with verbs and nouns (e.g., performing audits, designing portals, troubleshooting a server, managing software development, etc.) – and many people are very direct about their responsibilities/what they do, and we can take advantage of this and target these statements with proximity/semantic searching.

Semantic searching can also be used to specifically defeat the false positives associated with large skill summary/technology lists in resumes.  Most of these summary sections are just that – lists of technologies - and they are not sentences with subjects and verbs. Creating Boolean search strings employing a proximity search operator such as NEAR can essentially eliminate “hits” of search terms buried in lists because they are not sentences with nouns and verbs (e.g., developing applications in Java, supervising accountants, maintaining cancer registries, etc.) and they will not return results of search terms mentioned by themselves.

If you are interested in the concept of semantic search for sourcing and recruiting, stay tuned, as I have 2 more posts focused on semantic search coming in January.

Happy New Year!

On Tuesday, December 23, 2008 I wrote a “Tutorial Tuesday” post on www.recruitingblogs.com titled, “What is Semantic Search?,” explaining the concepts of semantic search with regard to how it can be leveraged effectively by sourcers and recruiters. Now, I am not sure if the fact that it was posted the day before Christmas eve had anything to do with it, but I only received 2 comments on the post so I am not exactly sure how many people actually had the chance to read it.  As such - I am posting it here in its entirety because while semantic search is not well known or understood, it can be powerfully applied to sourcing and recruiting efforts.

When I talk about semantic search, I don’t mean the semantic web (which I still think is a LONG way off), I am referring to user-generated and defined semantic search. In other words, a sourcer or recruiter creating Boolean search strings that go beyond simply trying to match the words themselves and attempting to delve into the meaning implied by the words.

In linguistics, semantics refers to the study of meaning, as inherent at the levels of words, phrases, and sentences.

The vast majority of sourcers and recruiters create Boolean search strings that simply return a collection of words – words that do not have any associative meaning and that are not guaranteed to be relevant with regard to the intent of the search. Relevance can be defined as the extent to which a search result matches the information need based on the intent of the person executing the search. Highly “relevant” results = results that match exactly what the searcher is looking for.

Most sourcers and recruiters are actually trying to find people that have specific skills and experience. 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.). For example, a knowledgeable sourcer or recruiter knows that documents with the word “account” mentioned close to the word “executive” will often have a different meaning and relevance than documents that simply mention the words “account” and “executive” located anywhere within them.

This is the critical difference between the semantic similarity between a search and its results vs. the lexical similarity between a search and its results. In other words – when the search results match the intended MEANING of the search, there is a semantic similarity between the search and its results. When search results simply match the search terms but not the intended meaning of the search, there is a lexical similarity (the words match) between the search and its results.

Semantic search can best be achieved through the use of search interfaces and engines that support proximity searching. Proximity search functionality allows a sourcer or recruiter to control how close specific words are mentioned in relation to other words.

When you are able to control the proximity of words to each other, you can take advantage of linguistics and sentence structure to look for verbs mentioned in close proximity to nouns, which can imply taking action. If a resume mentions (configure OR configured OR configuration) – which are verbs – in close proximity to (router OR routers) – which are nouns – and within the same sentence, it is highly likely that the writer is talking about being responsible for configuring routers.

A sourcer or recruiter should not be satisfied to merely scan and read resumes of people who simply mention the words “configure” and “routers” somewhere in the resume – there are many people who can mention those words somewhere in their resume who have never been specifically responsible for configuring routers. The issue is that just because these words are found in a resume – the presence of the words themselves does not MEAN anything with regard to what the candidate has specifically been responsible for.

With the appropriate search interface/engine, sourcers and recruiters can craft semantic searches to find people who not only mention specific words such as “configure” and “routers”, but who have actually had experience configuring routers. Being able to control the proximity of words can enable recruiters to quickly get more results that are semantically relevant to what the recruiter is actually trying to find.

There are 3 main types of proximity searching – I will focus on what I think are the two most powerful – fixed proximity search and configurable proximity search.

Fixed proximity search functionality such as the “extended Boolean” NEAR operator enables users to search for words or phrases that are mentioned close to other specific words or phrases. The range of the NEAR operator is fixed, typically at 1-10 words.

Did you know that Monster supports the NEAR operator? Many people aren’t aware of this – but it’s the only major job board resume database that I am aware of to do so. Kudos to Monster! It is unfortunate that there are very few people who even know about the NEAR operator, and even fewer still who know how to utilize it to achieve semantic search.

Among Internet search engines – Google, Yahoo, Live, and Ask do not support proximity searching of any kind – only Exalead does, to my knowledge. As for Applicant Tracking Systems, I am aware that Bullhorn has integrated Lucene, a free and open source text search engine that suppports configurable proximity, into their search interface

Configurable proximity search goes one step further than fixed proximity, allowing a sourcer or recruiter to precisely control the maximum distance between specific search terms and to return even more relevant results than the NEAR operator. This is because the NEAR operator’s maximum range of 10 words can allow for some non-relevant results to be returned. The farther words are mentioned apart from each other, the less likely it is that they are semantically related. In fact, when two search terms are separated by 10 words, each could be mentioned in separate bullet points or sentences on a resume and be completely unrelated.

However, with configurable proximity, a sourcer or recruiter can choose the maximum distance between search terms. Although search engines supporting configurable proximity vary with their exact syntax, here is an example of a search looking for someone who has been responsible for administering Exchange servers: Windows AND Exchange w/5 admin* AND server*. That search can ONLY return results of resumes or profiles that mention Exchange within 5 words of any word starting with the root of admin (administrator, administration, administer, administered, etc.), regardless of order. A maximum distance of 5 words will dramatically increase the semantic similarity between the search’s intent and the search results because mentioning those 2 search terms at such a close range makes it more likely that they are mentioned in the same bullet point or sentence and thus more likely to be semantically related. Essentially, this search will only return results of people who specifically mention something about being responsible for administering Exchange in their resume.

Many sourcers and recruiters employing basic search tactics and strategies may unfortunately be simply throwing a bunch of keywords in a search – and as a result, end up reviewing large volumes of irrelevant results that simply match the search terms they entered (lexical match) in order to “get lucky” to find the few results buried among them that are relevant to what they are seeking. This is a huge time drain, is inefficient, and is low yield.

Experts at talent mining seek to craft Boolean search strings designed to reduce irrelevant “false positive” results, eliminating those of people who simply mention the words they are searching for somewhere in their resumes or profiles, and go beyond the simple lexical match to achieve semantic search – finding people whose experience and skills match the essence of their search.

If you don’t already take advantage of the power of semantic search to quickly find more relevant results when creating your Boolean search strings, now is the perfect time to set it as a resolution for 2009. Make it a goal to move beyond simple buzzword matching and create Boolean searches that target people more based on what they DO, rather than just the words they use in their resume.

Most sourcing, recruiting, and staffing professionals are familiar with the “standard” Boolean operators of AND, OR, and NOT. However, I have found that few are familiar with “extended” Boolean functionality, such as proximity (or adjacency) and term weighting.

Beyond Basic Boolean

Extended Boolean offers sourcers and recruiters significantly more control, power and precision when executing searches, and in the hands of an expert – extended Boolean can enable semantic search. Semantic search uses the science of meaning in language to produce highly relevant search results rather than have a user sort through a list of loosely related keyword results.

Relevance is Key

Ultimately, any sourcing or recruiting professional knows that what’s most critical in running Boolean searches on the Internet, a job board, or in an internal resume database, is getting relevant results. According to Wikipedia, “relevance” denotes how well a retrieved set of documents (or a single document) meets the information need of the user.

For sourcing and recruiting, relevant results are typically defined as resumes or profiles of (or information about) potential candidates whose experience and capabilities closely match the hiring profile or job opening that the sourcer or recruiter is trying to find candidates for.

I’d argue that the value of any source of information (resume database, the Internet, etc.) lies less in the information contained within, and more in the ability of a user to extract out precisely and completely what the user needs – finding and retrieving any and all appropriately qualified candidates. Information has no value to you if you are unable to find it and take action on it.

So how can extended Boolean help sourcers and recruiters find more relevant results? Let’s take a look at term weighting first.

Variable Term Weighting

Talented sourcers and recruiters know that not all terms are equally important in a query. In most queries and searches, certain search terms are more important than others. When running standard Boolean queries, all search terms are considered/weighted equally. Unfortunately, many search engines and database search interfaces simply assign relevance to results by the number of search term “hits” in each document. In most cases, the simple frequency of search terms does not correlate to relevant results. This is where the derisive description “buzzword matching” comes from, most often used to denote that there is little skill involved in running Boolean searches counting matched keywords.

Using an Information technology hiring profile as an example – if a sourcer was looking for candidates who have significant experience administering Windows servers and Exchange email servers they might create a simple Boolean query such as this: Windows AND Exchange AND server* and admin*. That search is highly likely to return and rank candidates who are Windows systems administrators who mention Windows many times in their resume/profile and happen to mention Exchange once or twice as highly relevant because of the number of “hits” for Windows – which is by nature a very common term in resumes. This would leave the sourcer with having to sort through a large volume of results to find the candidates who actually have been primarily responsible for administering Exchange servers as well as Windows servers.

Search engines that offer users the ability to assign different weights to each search term enable sourcers and recruiters to move beyond simple buzzword matching and take control of the relevance of the results. Essentially, with variable term weighting you can assign a number value to words to increase their weight when ranking retrieved documents – which does not change the TOTAL number of results, but the ORDER of the results.

Using the same example as above, a sourcer using a search engine that supports variable term weighting could create a Boolean search string such as this: Windows AND Exchange:30 AND server* and admin*. That Boolean query will pull the same number of results as the first search that had no term weighting – however, it will sort and rank the results heavily favoring resumes/profiles that mention Exchange more often in relation to the other search terms, increasing the likelihood that the sourcer can quickly identify candidates who have had experience being responsible for administering and supporting Exchange servers. By employing variable term weighting, the sourcer has increased the relevance of the results.

Now, let’s take a look at proximity functionality:

Proximity

Proximity search functionality enables a user to search for specific terms that are mentioned close to other specific terms. An adept sourcer or recruiter knows that documents with the word “computer” mentioned close to the word “science” will often have a different meaning and relevance than documents that simply mention the words “computer” and “science” anywhere within them.

There are 3 main types of proximity searching: fixed proximity, variable proximity, and adjacency. For the purposes of this post – I will focus only on fixed and variable proximity.

Fixed Proximity

Fixed proximity is most commonly represented by the NEAR operator. The search engines that do recognize and support the NEAR operator typically define NEAR proximity as within 1 to 10-16 words (specific search engines can differ – check their documentation).

Using the example of a Windows and Exchange administrator, a sourcer could craft this search using the NEAR operator: Windows and Exchange NEAR admin* and server*. That search will ONLY return results of resumes/profiles that mention Exchange within 1 to 16 words of any word starting with the root of admin (administrator, administration, administer, administered, etc.). Being able to control the fact that Exchange MUST be mentioned within close proximity to admin* will dramatically affect and improve the relevance of the search results, typically returning results of candidates who either have a title using both terms and/or candidates that talk about being responsible for Exchange administration.

• Managed & administered more than 300 Exchange Servers
• Provisioned & administer multiple Exchange 5.5/2003 servers
• Not only are there administration duties for Exchange and Blackberry…
• Exchange/RightFax administrator
• Installing, Configuring, and Administering Microsoft Exchange 2000 Server
• Administer a Microsoft Exchange 2003/2007 environment
• 8+ years of expertise as a System Administrator in Windows 2003 family, Windows 2000 family, MS Exchange 5.5, MS Exchange 2000, and Exchange 2003
• I am proficient with the following skills; planning, installation and administration of Windows Active Directory, Windows Servers, Exchange Server
• Windows Server Support, Active Directory,Exchange Server 2000, 2003 administration and Blackberry Server administration
• Administer Exchange 2003 Server for corporate email

As you can see, being able to control the proximity of specific search terms essentially increases the likelihood of returning results of candidates who have had administrative responsibility for Exchange servers, effectively increasing the relevance of the results.

Fun fact:

• Did you know that Monster and Exalead support the NEAR operator?

Configurable Proximity

A search engine that supports configurable proximity affords users the ability to precisely control the distance between specific search terms. This can produce even more relevant results than the NEAR operator, because the NEAR operator’s maximum range of 10-16 can allow for some non-relevant results to be returned. The farther words are mentioned apart from each other, the less likely it is that they are semantically related. In fact, at 10-16 words, each could be mentioned in separate bullet points or sentences on a resume and be completely unrelated.

However, with configurable proximity, a sourcer or recruiter can choose the maximum distance between search terms. Although search engines vary with their exact syntax, here is an example of the Windows and Exchange admin search using configurable proximity: Windows and Exchange w/5 admin* and server*. That search can ONLY return results of resumes or profiles that mention Exchange within 5 words of any word starting with the root of admin (administrator, administration, administer, administered, etc.), regardless of order. A maximum distance of 5 words will dramatically increase the relevance of the search results because mentioning those 2 search terms at such a close range makes it more likely that they are mentioned in the same bullet point or sentence and thus more likely to be semantically related. Essentially, this search will only return results of people who specifically mention something about being responsible for administering Exchange at least once in their resume. By employing this kind of search, a sourcer is actually performing a semantic search, as they are looking specifically for people who talk about having a particular responsibility – not just looking for documents that contain words.

Fun facts:

• Did you know that Exalead supports configurable proximity searching?
• Did you know that you can integrate a free, open source search engine that supports configurable proximity and variable term weighting into your ATS or resume database? Check out Lucene.

Conclusion

Hopefully you can see how being able to control the proximity of two search terms can yield results that are FAR more relevant than results that simply mention the two terms anywhere in a document or form – this is the critical difference between the semantic similarity between a search and its results vs. the lexical similarity between a search and its results.

There are countless ways you can apply extended Boolean functionality such as variable term weighting and proximity searching to nearly any industry/hiring profile to create searches that return highly relevant results - results that are more relevant than those that can be acheived with standard Boolean logic. Using a search engine that supports both variable proximity and variable term weighting can empower sourcers and recruiters to quickly find large volumes of highly relevant results, increasing productivity and achieving JIT Talent identification and acquisition.