It’s official.

The SourceCon event at the Georgia Aquarium on Feb 9 & 10 will be the biggest event ever, with more attendees than any other SourceCon!

What that really means, other than proof that Atlanta is the center of the sourcing universe [sorry Seattle ], is that attendees will have more opportunity to network with, share best practices, and learn from other sourcers and sourcing leaders than ever before.

As you would expect, SourceCon will treat those in attendance with high-value keynotes and general sessions from industry luminaries such as:

• Aida La Chaux from Yahoo on sourcing through adversity
• Adam Lawrence of Alexander Mann Solutions on global sourcing
• Jim Stroud of Bernard Hodes on social & personalized search
• Eric Jaquith from SourceRight on how to stack the deck in your favor when it comes to sourcing
• Conni LaDouceur on phone sourcing best practices (and yes, you’ll hear recorded calls!)

In addition, there are breakout tracks for sourcing leaders and for sourcing practitioners lead by Charles Bretz, Shannon Van Curen, Shannon Myers, Cathy Henesey, Elaine Order, Justin Clem, Anne DeWys, Therese Hightower, Cathy Henesey, and Atlanta’s own Chris Havrilla, covering topics such as ATS/candidate databases and social and mobile sourcing.

So what will I be doing there?

Well, this will be my 5th SourceCon, and instead of presenting a keynote, I am honored to be the official Conference Chair, so I will be leading off the event, facilitating discussions, and wrapping up each day.

When I kick off the event, I plan to address the current state of sourcing and what I believe to be the future of sourcing.

I also plan to bring up the recent Sourcing Compensation Survey which has some fantastic data, and an excellent infographic.

Register for SourceCon

There’s still time to register and attend – and you can take advantage of my special discount code (find it in the image above) to save 10%.

If you’re local or can get to Atlanta by Thursday – you won’t want to miss this sourcing event!

For a while, there was an interesting little method for revealing the full name of 3rd degree and group connections on LinkedIn. However, LinkedIn has changed the “get introduced” functionality and UI for most people and effectively eliminated that method (albeit unintentionally, IMO).

Oh well – it was easy and fun while it lasted.

Fortunately, I’ve recently become aware of another way of revealing the full names of 3rd degree connections on LinkedIn with a less-than-premium account that I would like to share with you.

But before we get to that, I’d like to cover some basics as well as some things I have been noticing about LinkedIn – I believe they may be tinkering with free access profile visibility.

Oh, and if you’re on the fence about attending SourceCon in Atlanta next week, it’s shaping up to be the largest in SourceCon history, and you still have time to register and get a 10% discount using my SC12GC code.

LinkedIn Public Profile Search to View Full Names

Now that the nifty “get introduced” full name visibility trick is seemingly dead, people without LinkedIn Recruiter access can of course still grab one or more unique phrases from 3rd degree and group-only LinkedIn connections and throw them into Bing or Google to find their public profile and thus their full names.

For example, I can take the headline phrase and couple it with the location phrase from a LinkedIn search result…

…and enter this into Bing: “Senior Software Development Manager, IBM” “Ottawa, Canada Area”, and here’s what I get:

You can do the same thing with Google, but Google’s first result isn’t the profile we’re looking for – that’s why I favor Bing for this technique. Google seems to index all of the nooks and crannies of LinkedIn yielding “dirty” and irrelevant results when searching for LinkedIn profile word/phrase combinations.

Is LinkedIn Tinkering with Public Profile Visibility?

I am sure I am not alone in noticing changes to the standard LinkedIn X-Ray search results.

For example, while you could see the full names of 3rd degree and group-only connections after clicking on a search result even if you were logged into LinkedIn, now I am finding that if I am logged in, once I click on a Google or Bing LinkedIn site: search result, LinkedIn recognizes that I am not connected to the person at the 1st or 2nd degree and thus only shows me the first name and last name initial.

Here is a screenshot of my Google search and the result – the full name is displayed:

However, once I click on the search result, if I am logged into LinkedIn, I only see the first name:

I know for a fact this has not always the case – I’ve been training people on this for years and I’ve done this 1,000′s of times.

Big deal?

No – but I still find it interesting to notice changes like this because it means what we have all been wondering about (fearing?) may finally be coming – evidence that LinkedIn is working to limit or close off free access to LinkedIn data.

In fact, I’ve also been running into some seemingly random funny business with LinkedIn public profile URL’s. I have encountered a number of instances in which I’m using either Chrome or IE and I click on (or cut and paste) a public profile link, LinkedIn tells me that the profile is not found, when I know it does exist because I just looked at it.

For example here’s one I found while writing this post:

When I click on that link or cut and paste it in Chrome or IE, this is what I get:

So far, there is no pattern to it that I can tell – and I have only begun to notice this in that past few weeks.

I am not sure what it is indicative of, but wondered if anyone else has encountered this and might have some insight.

Using Alumni Search to View Full Names of 3rd Degree Connections on LinkedIn

Last week, Patrick Ryan, a former colleague with whom I stay in touch, sent me an email with something he had discovered on LinkedIn. I asked him if he minded if I wrote about it and he said no, so here we go.

If you scroll down your LinkedIn home page, you’ll find the “Just joined LinkedIn” section with Colleagues and Alumni.

If you click on the school name under Alumni, you’ll be taken to an attractive dashboard with some interesting information.

From here, you can change the years attended, show alumni that don’t show a graduation date, and of course search.

When you configure your search and see some 3rd degree connections without full names, this is what they will look like – first name only:

If you click on “Connect” and you’re using either Chrome, Firefox or Safari (not IE – sorry), you’ll get a pop up that will allow you to send the person an invitation, and it will also show the person’s full name.

Okay – I am sure some of you are thinking, “Cool – but this is extremely limited because I can only search alumni from my school.”

Sure, it’s limited, but let’s just say you can similarly search through any school that’s in the education section of your LinkedIn profile.

Oh, and each school as a school ID. For example, mine is 18570.

Final Thoughts

You don’t need to have a premium LinkedIn account to view any public profile and see the full names of people who are not 1st or 2nd degree connections.

However, it’s not safe to assume that the ways in which we can currently search for and view the information on LinkedIn profiles beyond our network will remain unchanged.

In fact, I believe we can expect LinkedIn to develop ways that limit the ability to find and view certain info on LinkedIn profiles for free – and why shouldn’t we? LinkedIn isn’t a non-profit, and just as any other for-profit company, it’s their prerogative to look for ways to make money and to reasonably limit giving away too much for free.

Will LinkedIn read this post and change the ability to view full names of 3rd degree connections via alumni search?

Yes, they will read this post (Hi LinkedIn team – special shout-out to the Ninja!).

Maybe they will do something to change the appropriate functionality.

If they do, I’m happy to have helped.

Oh – and be sure to check out the comments.  Several readers offered other ways of revealing the full names of 3rd degree connections on LinkedIn.

Anything worth doing is worth measuring, and sourcing isn’t exempt from this.

If you want to know which method of sourcing has the highest ROI in terms of enabling a person to find more of the right people more quickly, then you’re in luck – because that’s what this post is about.

Human capital data comes in many forms – resumes, social network profiles, blogs, bios, press resleases, etc. – and I have found that a key and critical aspect of sources of human capital data that many people fail to formally recognize is the depth and completeness of the data that can yield information through review and analysis.

When it comes to leveraging information systems such as the Internet, applicant tracking systems, social networking sites, job board databases, etc. for sourcing and recruiting – the operative word is “information.”

Data is the lowest level of abstraction from which information can be derived. For data to become information, it must be interpreted and take on a meaning.

Generally, the quality and amount of information that can be gleaned from any particular source is directly linked and limited to the quality and amount of data present to be reviewed and analyzed. How useful is an information system supported by only a small amount of limited data?

In this post, I will:

• Review the major sources of human capital data
• Examine sourcing return on time invested
• Explore the potential candidate’s point of view
• Ask you to take a quick sourcing test

Ready?

ATS, Job Board Resume Databases

Resumes typically represent the deepest source of human capital data.

While the accuracy of them can be argued (albeit no differently than social media profiles) – most resumes contain significant and specifically professional information about the people who wrote them.

Even when poorly written, most resumes contain:

• A summary of experience
• Objectives that can give you insight into the types of opportunities they are interested in
• A work history that can give you an idea of their capabilities based on their past responsibilities and experience at specific companies, as well as an educated guess as to their desired compensation
• A full address, which can be critical in making an educated guess at whether or not they might be open to a particular commute

LinkedIn Profiles (CV’s?)

LinkedIn is the one stand-out social networking application that has a decent number of profiles with deep human capital data.

Although not a resume database, you can typically find (and thus search for and target) more employment qualification-related information than anywhere else outside of an actual resume database.

While LinkedIn calls them “profiles,” and some contain very little information other than 1 title and 1 employer, some LinkedIn users fill their profiles out just as they would their resume.

In fact, with the employment market in relatively bad shape, there are a number of articles advising job seekers to do exactly that – fill out their profile as they would a resume.

And now, LinkedIn even offers the ability to convert your LinkedIn profile into a resume(fantastic move, by the way!).

It also doesn’t hurt that LinkedIn has a robust search interface, supporting full Boolean logic as well as a number of LinkedIn-specific advanced search operators. Great search interface + deep human capital data = highly leveragable information system for talent identification.

Of course, it can’t be overlooked that there are more incomplete and shallow LinkedIn profiles than there are complete and fully fleshed out profiles, so all is not perfect in LinkedIn land.

Additionally, while LinkedIn has started to add some more specific location options for people to select (for example, my zip code gives me the option to select Alpharetta or Atlanta), many people still use their major metro area as the location on their profile (I do).

This can make it difficult to find people who are likely to be close to the location of the job you are sourcing for and thus “recruitable.”

What About Facebook, Twitter, and Google+?

While many people in the recruiting and staffing industry get REALLY excited about Facebook, Google+ and Twitter - I don’t.

Before you recoil in absolute horror that I haven’t jumped on the bandwagon with everyone else, let me say that I’m a big fan of leveraging  any/all social networking sites (provided your target talent uses them to a good degree, of course).

Yes, I they’re cool, and yes, I use them.

However, I refuse to get so blinded by their perceived potential and the hype in the sourcing/recruiting community that I fail to see their limitations.

You can certainly use Twitter, Google+ and Facebook to identify and contact potential candidates – there’s no arguing that. While Twitter is highly searchable, supporting Boolean queries and their own set of advanced search operators, Facebook isn’t (although it does offer you access to the largest single repository of people on the planet), and Google+ isn’t nearly as searchable as it should be given that it’s a Google creation.

However, regardless of “searchability,” none of those sites offers much professional data about the people who use them, or at least not the right types of information that can help a sourcer or recruiter gain any significant insight into specific skills, experience (including precise responsibilities and capability as well as overall years and career progression), and specific location.

You might get lucky to see a title on Facebook, Twitter, or Google+, and you might find people talking about their line of work, but the people who do mention titles and in some cases even employers is the vast minority.

Lastly – when it comes to social networking sites like Google+, Facebook, and Twitter, even when people do mention something work related online that can enable you to try to guess what it is they do, in many cases they do so using non-standard terminology, which poses an additional challenge to talent identification.

Shallow Human Capital Data

Facebook, Twitter and Google+ can be effectively leveraged for employer and recruiter branding, marketing, online community development, and socializing job opportunies (that’s social media speak for “job posting”) – which are largely passive methods of talent attraction.

However, as shallow sources of human capital data, Facebook, Twitter and Google+ are not particularly effective for active candidate identification.

When I say “active candidate identification,” I’m not referral to job seeking status (people actively seeking employment) – I’m referring to the process of actively searching for and identifying candidates with specific experience and qualifications that are highly likely to match specific hiring needs.

Posting jobs is a passive method of identifying potential candidates, because you post the job and then sit and wait for people to do the work of identifying themselves.

There is no doubt you can find and contact LOADS of people using Facebook, Twitter and Google+. However, in most cases, you have no real idea how much and exactly what kind of experience these people have prior to contacting them, and in many cases, you don’t know precisely where they live.

Just because they list that they have their CPA, or that they belong to a nursing association, or they are a “fan” of a PHP developer page - it certainly does not guarantee you of anything beyond that.

Non-Resume Internet Research

Using Internet search engines such as Google, Bing, Blekko, et al, to search for and sift through human capital data can definitely produce results.

I won’t argue that. However, once you go beyond resumes (the deepest sources of human capital data), you quickly enter the shallow end of the human capital data pool - press releases, blog posts and comments, articles, etc.

I would never suggest that these shallow data sources can’t be leveraged for sourcing and recruiting – but my point is that the intrinsic probability that any particular non-resume search result is qualified for your hiring needs is LOW.

This is because less data means less information available to be gleaned about the potential candidate – leaving us with little to no idea as to their professional experience and qualifications, and even specific location in many cases.

Expect a Return on your Time Invested

Maybe some sourcers and recruiters like to find and contact lots of people because they get paid to just be social and make lots of friends online.

Maybe some companies think it’s productive and cost effective to sift through and contact large quantities of people who aren’t qualified for, would not be interested in, and/or would not commute (or relo!) to the opportunity they are being sourced/identified for.

I certainly don’t! Who does anyway?

Wait – please don’t raise your hand (not you – that other person).

As shallow sources of information, Facebook, Twitter, Google+ and practically all sources of non-resume human capital data on the Internet simply don’t have much professional-experience/qualification-relevant information.

Less and incomplete data doesn’t really make for a heavily leverageable information system.

At least not when it comes to talent identification where it’s more than helpful to know a little bit about someone’s experience before you contact them.

Value to the Candidate?

Candidates generally appreciate being contacted for opportunities that are in their “ballpark” when it comes to location and responsibilities.

Most candidates don’t appreciate being contacted for opportunities that aren’t.

Think about this for a second – what VALUE are you providing to people that you find and contact using shallow sources of human capital data when they are in fact not even remotely qualified or interested in your opportunity?

Most people don’t appreciate being contacted by recruiters only to end up being used as a tool in your networking/referral recruiting efforts because you didn’t have enough information about them to possibly provide anything of value to them.

Yes, I remember the days of just picking up the phone and calling people with little to no information – but take a second to answer this question: Is this kind of practice and process the best and highest ROI method of sourcing and recruiting?I think not.

Critical Candidate Matching Variables

Deeper and more detailed human capital data enables more precise and controlled searches, allowing sourcers and recruiters to be able to make an educated decision to contact people based on capability and experience rather than blind faith or a guess based on perhaps a title alone.

With resumes or fully fleshed out LinkedIn profiles, a talented sourcer or recruiter can effectively control critical candidate variables such as location, potential opportunity match, and experience/capability – including years of experience, which can tie into compensation.

Sourcing Test: Which Person is More Likely to be Interested and Qualified?

Here is a dramatic and certainly more practical example of deep vs. shallow human capital data: If you were responsible for filling a position for a Business Analyst with energy industry experience and specific experience working on SAP projects and using UML, which of the following people has the higher probability of being both qualified and interested in your opportunity?

Person #1: LinkedIn Profile

Person #2: Twitter Bio

The contrast is dramatic.

The LinkedIn profile is essentially filled out as completely as a resume would be, and as such, we can feel confident when contacting this person because their experience appears to closely align with our opportunity, and even if they aren’t recruitable, they’ll have to admit the opportunity was relevant.

The Twitter profile mentions the title of “Business Analyst,” but little else – we have no idea as to this person’s industry or project experience. While we can cross reference the Twitter Bio with LinkedIn, when doing so, we can see by looking at her profile that she does not appear to have any energy industry experience, and we cannot tell if she has any SAP project or UML experience.

If you had a choice between using either an information system that had shallow data on the people contained within, or an information system that had deep data on the people contained within - and you could only choose one – which would you choose and why?

I know which one I would choose – all things being equal, I would choose the information system with the deep and more complete human capital data.

That way, I can run creative and effective queries to search for, find, and contact people based on specific experience and qualifications. Why would anyone choose any different?

Final Thoughts

You can find and hire people by searching any source of human capital data – resume or otherwise.

However, searching Facebook, Twitter, Google+, blogs, the Internet and other similarly shallow sources of human capital data requires a higher amount of effort for a smaller return – what I call low yield sourcing and recruiting.

While there is undoubtedly more shallow human capital data than deep human capital data, does it sound like a good idea to go out of your way to focus on low yield sourcing and recruiting?

When it comes to proactive candidate sourcing (e.g., searching for people and not posting jobs and waiting for responses), I’d argue that the deep sources of human capital data such as resume databases, applicant tracking systems, LinkedIn, and Internet resumes are responsible for producing 80% of the search based sourcing and recruiting results (hires).

Conversely, the shallow sources of human capital data such as Facebook, Twitter, Google+ and and non-resume Internet research produce 20% of the active-search based sourcing and recruiting results. You essentially have two paths:

1. Find and contact more uninterested and unqualified people
2. Find and contact more interested and qualified people

Which one will you take?

Does your employer give you a choice?

If you have nearly any tenure in HR, sourcing or recruiting, you’ve probably heard something about “semantic search” and perhaps you would like to learn more.

Well – you’ve found the right article.

As a follow-up to my recent Slideshare on AI sourcing and matching, I am going to provide an overview of semantic search, the claims that semantic search vendors often make, explain how semantic search applications actually work, and expose some practical limitations of semantic search  recruiting solutions.

Additionally, I will classify the 5 basic levels of semantic search and give you examples of how you can conduct Level 3 Semantic Search (Grammatical/Natural) with Monster, Bing, and any search engine that allows for fixed or configurable proximity.

But first – let’s define “semantic search.”

What is Semantic Search?

Semantics is the study of meaning, inherent at the levels of words, phrases, and sentences.

Semantic search is most often used to describe searching beyond the literal lexical (exact word for word) match and into the meaning of words and phrases at the conceptual and contextual level, and sentences at the grammatical level.

When sourcing candidates, semantic search can be achieved at the conceptual level when a search for a specific term (e.g., Java) also yields matches on related terms (e.g., J2EE, EJB, servlets, etc). – words that are related conceptually.

As another example, in the healthcare space, a semantic search for “cancer” could also produce positive hits on terms such as oncology, lymphoma, tumor, etc.

Words and phrases by themselves can be somewhat ambiguous, but are less so when taken in context - using surrounding words or passages that can shed light on the intended meaning.

For example, “Java” is a software programming language, but it is also used to refer to coffee, and it is also an Indonesian island. A quick Twitter search for “Java” will typically net you a mix of references to Java. By reading each tweet and the text surrounding “Java,” we can easily disambiguate the reference to “Java” and divine the intended meaning.

Below you can see Java referenced on Twitter in 3 very different ways in 3 successive tweets, and the context tells you how to interpret the meaning of “Java” in each one:

Why Should HR/Recruiting Professionals Care about Semantic Search?

There is more information available about more people today than ever, and the volume is only going to increase and the rate at which is accumulates is accelerating.

Sifting through an ever-increasingly large amount of human capital data in the form of resumes, social media profiles (LinkedIn, Twitter, Facebook, etc.), blogs and other sources is a significant challenge.

The promise and potential of semantic search is that it can help you more quickly and easily cut through massive volumes of potential candidate information to help you find more of the right people faster than standard methods.

Choose Your Own Adventure!

Now that you understand semantics and the basic concepts of semantic search, you have a choice:

1. If you don’t particularly care to get into the details of how solutions that claim to use semantic search actually work and achieve their claims, you can skip all the way to the end for a presentation on the 5 Levels of semantic search. In that presentation that you can find a couple of examples of how to achieve Level 3 semantic search with Monster or any search engine that offers proximity search, which allows you to control how close your search terms are to each other.
2. If you currently use a matching application that claims to leverage semantic search (e.g., Monster’s 6Sense), if you’re considering purchasing/implementing such a solution, or if you’re just curious how these kinds of applications achieve their claims, don’t skip ahead and continue reading.

Semantic Search Claims for Sourcing and Recruiting

Many vendors are quick to explain that their semantic search solution can help you and/or (wink) your team to “stop wasting time trying to create difficult and complex Boolean search strings”, and instead, let “intelligent search and match” applications do the work for you.

Some claim that “a single query will give you the results you need – no more re-querying, no more waste of time!”

Going further, semantic search solutions for the recruiting industry commonly state that their offerings:

• Understand titles, skills, and concepts
• Automatically analyze and define relationships between words and concepts
• Intuit and infer experience by context
• Perform pattern recognition
• Perform fuzzy matching

Sounds Great – But How Do They Really Work?

Over the years, I’ve had many people attempt to sell me on the benefits of semantic search when it comes to sourcing potential candidates, and I have also had the opportunity to use and evaluate quite a few semantic search solutions, including pretty much all of the usual suspects in the space.

My experience and skill with regard to human capital data information retrieval information retrieval affords me some unique insight as to how the technologies and techniques semantic search vendors utilize to make their claims actually work, as well as their limitations specific to human capital data. More on that last bit later.

First, let’s get into how semantic search applications for recruiting actually work.

When semantic search vendors make claims that their applications can automatically understand titles, skills, and concepts, analyze and define relationships between words and concepts, intuit and infer experience by context, perform pattern recognition and fuzzy matching, they are typically using 1 or more of the following to do so:

Resume Parsing

Parsing slices and dices resumes and extracts useful information contextually based on the structure of most resumes.

A good parser can take a resume and break it down to its component parts and “understand” a person’s experience.

Resume parsing can be used to extract skill words and differentiate between terms mentioned in skills summaries vs. those that are mentioned in the body of the resume – the latter having a higher probability of being indicative of real experience. Resume parsers can also typically extract titles and employers and some can even reliably identify the most recent title and employer.

Solid parsing technology can correctly identify addresses and education information and “realize” that “George Washington” in an address is likely a street name, but in an education section a University.

Some parsers can even determine current vs. dated experience with specific skills, as well as automatically calculate years of experience with specific skills, management, and overall years of work experience based on date analysis. Being able to control years of experience can help find people who aren’t under- or overqualified or not likely to be in the compensation range of the opening you are sourcing/recruiting for.

Resume parsing can result in highly structured data, which can enable a recruiter to move beyond free text search and to search for information contextually in specific sections/fields, such as current title, current experience, education, etc.

A more automated way of achieving semantic search via parsed resume data is to take basic search terms entered by a sourcer or recruiter and weight search results based on recency of related titles and experience, based on data parsed and identified as more recent, as well as calculated years of experience (e.g., Java and related terms mentioned in most recent work experience, dated ’9/06 to Present’).

So now you know that when you hear that a semantic search application can “automatically understand titles and skills” and can “intuit and infer experience by context,” not only do you know what they’re talking about, you know at least one of the ways they try to make good on that claim.

Taxonomies and Ontologies

Some semantic search solutions for recruiting leverage ontologies and taxonomies.

Taxonomy is the science which deals with the study of identifying, grouping, and naming things according to their established natural relationship. An ontology is a “formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts.”

As complex as those definitions may sound, they are really quite easy to understand when it comes to how vendors utilize taxonomies and ontologies to achieve semantic search.

Taxonomies and ontologies are leveraged by semantic search solutions for recruiting and staffing as a back-end list of keywords organized by concept and relationship so that when you search for a term or phrase, the solution can compare your search against terms and phrases it “knows” are conceptually related.

A common taxonomy used in recruiting solutions is a parent-child, hierarchical (directional, one way) taxonomy. Wikipedia uses this simple way of explaining the parent-child relationship: A car is a subtype of vehicle, so any car is also a vehicle, but not every vehicle is a car.

Hierarchical Taxonomy

With a hierarchical taxonomy for accounting terminology, if you searched for “SOX 404,” you should get positive hits and relevance ranking from the term “SOX 404″ as well as “accounting,” because the system can recognize that “SOX 404″ is an accounting-related “child” term/concept tied to the “parent” term “accounting.”

In a true hierarchical taxonomy, if you searched for “accounting,” you should only get positive hits and ranking on the term “accounting” and not on mentions of “SOX 404,” because not all accounting-related work involves SOX 404.

In other words, SOX 404 is accounting-related work, but not all accounting work is SOX 404-related.

Conceptual Search

A semantic search solution using a hierarchical taxonomy can help you find terms and phrases other than the ones you specifically searched for, because they compare your search terms with the taxonomy and return results that not only mention your keywords, but also related terminology.

This is a form of “conceptual search” – you search for 1 term, and you can get results mentioning all related concepts as well as your original search term.

In addition to hierarchical relationships, semantic search solutions may also perform conceptual searching based on synonymous terms and phrases.

For example, if you searched for “Director of Tax,” a well developed taxonomy would also return results for all of the title variants you didn’t actually search for, but are the same, such as “Tax Director,” “Director, Tax,” etc. This form of conceptual search can be useful for finding common abbreviations for phrases, such as CPA and “C.P.A” from a search for “Certified Public Accountant,” and vice versa.

A comprehensive taxonomy can be especially helpful for Information Technology sourcers and recruiters, as it can be difficult to know or even remember all of the various ways certain technologies can be referenced (SQL 2008, SQL Server, MSSQL, etc.).

Statistical Methods

Rather than relying on pre-built taxonomies to define relationships between titles, terms and concepts, some semantic search solutions use complex statistical methods in an attempt to automatically “understand” language and relationships between words.

While I am not aware of any semantic search vendor supplying solutions to the recruiting industry that publicly explains their statistical methods, thankfully Google gives us a tiny bit of insight of how such an approach works.

Google has found that keywords with the same or similar meanings in a natural language sense tend to be “close” in units of Google distance, while words with dissimilar meanings tend to be farther apart.

Here is the equation for the Google distance, which is a measure of semantic interrelatedness derived from the number of hits returned by the Google search engine for a given set of keywords.

That was easy, right?

Semantic Clustering, Machine Learning, Pattern Recognition – Oh My!

I don’t pretend to understand semantic clustering and machine learning at the technical level, but I do have a good understanding of what they are used for and how they work at a high level, specifically with regard to sourcing and matching candidates from human capital data.

Semantic clustering is a non-interactive and unsupervised machine learning technique seeking to automatically analyze and define relationships between words and concepts.

For candidate sourcing purposes, algorithms are created to automatically learn to recognize complex patterns, “learn” and draw relationships from human capital data (resumes, social network profiles, etc.).

Rather the relying on a static taxonomy, semantic clustering allows for dynamic concept matching.

Based on statistical analysis/algorithms and pattern recognition, an application can “learn” that C# is related to .Net, due in part to keyword frequency and proximity that it has analyzed across thousands to millions of documents.

A query cloud offers an excellent visualization of semantic clustering – you can see and choose from a group of terms and phrases that the semantic search solution has determined to be related to your search term.

Here is an example of a query cloud for C#:

While semantic clustering can quickly and easily find related terms, the question has to be asked of whether or not the related terms are actually relevant. Only the person conducting the search can make that determination.

Fuzzy Logic

When an application claims to perform fuzzy matching, it is apply fuzzy logic to the search, which finds approximate matches to a pattern in a string.

Fuzzy logic is especially useful to automatically search for slight phrase variations and word misspellings. Most sourcers/recruiters do not take the time to search for misspellings, and understandably so as it is quite laborious. However, a good fuzzy matching solution will find your exact search terms as well as any slight spelling variation, intentional or unintentional.

If you don’t search for misspellings, you’re missing people:

The 5 Levels of Semantic Search

Now that you have a basic understanding of the concept of semantic search and how applications using semantic search actually work, I’d like to introduce you to what I believe are the 5 basic levels of semantic search.

Intended for HR professionals, sourcers and recruiters, this presentation explains and explores the concepts of semantics and semantic search, including the 5 Levels of Semantic Search: Conceptual Search, Contextual Search, Grammatical/Natural Language Search, Inferential Search, and Tagging.

You’ll also see some examples of how you can achieve Level 3 semantic search using Monster (classic search) or any search engine that allows for fixed or configurable proximity search.

Semantic Search for Recruiting: The Good

I love technology and anything that can make me better and faster at what I do. Semantic search solutions for sourcing candidates can provide many benefits, including:

• Reducing the time to find relevant matches
• Lessening or eliminating the need for recruiters to have deep and specialized knowledge within an industry or skill set
• Reducing and even eliminating time spent on initial research
• The ability to go beyond literal, identical lexical matching
• Leveling the playing field for those with less sourcing experience or ability
• Making an inexperienced person look like a sourcing wizard
• Boosting teams with low search/sourcing capability
• Working well for positions where titles effectively identify matches and where there is a low volume and variety of keywords
• Working well for organizations with a high volume of unchanging hiring needs

Semantic Search for Recruiting: The Bad

On the other hand, you should be aware of some issues associated with blindly trusting semantic search solutions, including:

• Just because terms are related, it doesn’t automatically make them relevant to the search
• Removing thought from the talent identification  process
• The danger of eliminating the need for recruiters to understand what they’re actually searching for
• Difficulty with information technology, healthcare, and other sectors/verticals with ever-changing technology and terminology
• Finding some people, but eliminating and/or burying others
• Finding the best matches based on keywords present, as opposed to the best people
• The inability to search for what isn’t explicitly stated - applications will only return results that mention required keywords and their variants
• The fact that many people have skills and experience that are simply not mentioned anywhere in their resumes and thus they cannot be retrieved via any direct search method
• They level the playing field – if competing companies use the same software solution, they will both find (and miss!) the exact same people
• The fact that a single search cannot find all of the best people – every search both includes and excludes qualified candidates
• They can favor keyword rich resumes/profiles, yet keyword poor resumes/profiles may in fact represent better candidates that keyword rich resumes

Semantic Search for Sourcing & Recruiting: The Bottom Line

The potential of semantic search for talent identification and acquisition is powerful and exciting!

However, it’s important to realize that with technology that’s been on the market for over a decade, sourcers and recruiters have already been able to “manually” achieve Levels 1-4 semantic search for a while now, and there are some solutions available today that allow for searchable tagging as well (Level 5).

On the other hand, using software for automating semantic search/match can allow you to quickly, easily and somewhat reliably achieve Levels 1-2 semantic search, depending on the vendor/solution you choose. At this time, true Level 3-5 semantic search is beyond the reach of today’s semantic search/match applications (IMHO).

One of the main and inescapable problems with any automated semantic search/match solution is that human capital data is quite often incomplete and unstructured. Let’s face it – no company is looking to find people because they mention specific keywords and titles – everyone’s looking for their next great hire who has specific skills and experience which may not even be explicitly mentioned in a resume, on a LinkedIn profile, in a Twitter bio, etc.

Matching software can work with what’s there (text that’s present), but they can’t match on what’s not there (text that isn’t present). On the other hand, one thing that humans do incredibly well is instantly perform dynamic inference, more commonly known as “reading between the lines.” Perhaps at some point in the future, software will be able to somewhat reliably infer experience and capability beyond text that is present, but it can’t be done today beyond guessing (e.g., “Were you looking for _____________?”).

Food for thought – how would you like to explain to a candidate that the reason why they weren’t considered for a job was because your semantic search application didn’t think they were a match based on their resume? How would you feel if you were turned down in consideration for a job because a software solution didn’t “like” your resume? Do we really want to rely 100% on a software solution that seems to make our life easier when it can result in missing and altogether eliminating some of the best people available?

While software can retrieve and move data, data requires analysis to yield information and produce knowledge which can facilitate decision making. That’s why these solutions are referred to as Decision Support Systems - the operative word being “support,” because they don’t (and should not!) make the decisions for you – these solutions provide you with data to interpret for information to make an informed decision.

In the case of sourcing/recruiting – it’s deciding who to engage, screen, and potentially recruit.

Back in March 2010, I had the distinct honor of delivering the keynote presentation at SourceCon on the topic of resume search and match solutions claiming to use artificial intelligence in comparison with people using their natural intelligence for talent discovery and identification.

Now that nearly 2 years has passed, and given that in that time I’ve had even more hands-on experience with a number of the top AI/semantic search applications available (I won’t be naming names, sorry), I decided it was time to revisit the topic which I am very passionate about.

If you’ve ever been curious about semantic search applications that “do the work for you” when it comes to finding potential candidates, you’re in the right place, because I’ve updated the slide deck and published it to Slideshare. Here’s what you’ll find in the 86 slide presentation:

• A deep dive into the deceptively simple challenge of sourcing talent via human capital data (resumes, social network profiles, etc.)
• How resume and LinkedIn profile sourcing and matching solutions claiming to use artificial intelligence, semantic search, and NLP actually work and achieve their claims
• The pros, cons, and limitations of automated/black box matching solutions
• An insightful (and funny!) video of Dr. Michio Kaku and his thoughts on the limitations of artificial intelligence
• Examples of what sourcers and recruiters can do that even the most advanced automated search and match algorithms can’t do
• The concept of Human Capital Data Information Retrieval and Analysis (HCDIR & A)
• Boolean and extended Boolean
• Semantic search
• Dynamic inference
• Dark Matter resumes and social network profiles
• What I believe to be the ideal resume search and matching solution

As a reader of Boolean Black Belt, I’d looking for your feedback and input on a few things.

For example:

• What would you like to see me write more about?
• Would you like to write a post on Boolean Black Belt?
• What do you think of me changing my site to something other than “Boolean Black Belt?”

However, before I go any further down that rabbit hole, I’d like to thank you, give you some insight as to why I blog, and let you know about what you can expect from me in 2012.

First & Foremost – Thank You

As 2011 has come to an end, I find myself reflecting on the past as as well as looking forward to what 2012 will bring.

Most importantly, I want to thank you and the other 112,320 people who stopped by my blog in 2011, who came from 175 countries and viewed over 300,000 pages.

I owe special thanks to all of you who have commented on my posts, shared them with others, and suggested my site to peers, managers, and team members.

If I’ve ever shown you something you didn’t already know, or made you think or reconsider what you do already know,  I’ve accomplished one of my main goals in blogging, and it makes all of the hours of precious personal time I take on a weekly basis to publish my blog posts worthwhile.

Why Do I Blog Anyway?

This happens to be my 165th post since I started blogging back in October of 2008.

I write for many reasons, and monetizing isn’t one of them. I will never take a 2000 word post and split it into 4 500 word posts just to string my readers along.

I’d love to post content more frequently than once per week, but with a family and a full time job, blogging about recruiting, sourcing, and social media is essentially a weekend hobby.

A hobby is defined as “a pursuit outside one’s regular occupation engaged in especially for relaxation.” I’m not so sure blogging relaxes me, but it certainly doesn’t feel like “work,” as time flies while I am writing and it is definitely intellectually cathartic.

If I could sum up what I am trying to accomplish by blogging, I would have to use something Erik Weihenmayer said during his LinkedIn Talent Connect 2010 keynote: I want to “contribute to something extraordinary.”

While it may sound corny or ridiculous to some, I am actually trying to try and change the world of recruiting one post at a time.

How?

• By freely sharing with everyone what is common knowledge to only a few and what many would charge a premium for.
• By challenging both status quo (“traditional” recruiting) and hype (bright shiny objects) when it comes to all things recruiting, from talent discovery through acquisition.
• By serving as a “curve hunter,” introducing new concepts and pushing innovation to the global sourcing and recruiting community.

I don’t write to make people agree with me – I just want people to think instead of clinging to what they already know and are comfortable with.

Because if you’re comfortable, you’re not growing.

What’s Coming From Boolean Black Belt in 2012

I had a pingback a while ago from a blogger who praised the content of one of my posts, but complained about the length.

I took it as passive constructive criticism, and I took a moment to think about my blogging style.

Most of of my posts are in the 1500 word range, and some go over 2000. In the blogging world, that’s on the long side. I know that many people want “fun-sized” content (500 words or less) that they can consume in 30 seconds or so. However, some concepts can’t adequately be explained in 500 words or less, nor would 500 words do some topics proper justice. And as I stated previously, I don’t write to string people along – I would rather publish a 2000 word post than split it into 4 500 word posts.

There’s nothing more annoying to me than clicking on a link from a tweet that praises an article, and once I get to the article, I find it to be a 300 word “fluff” post, totally devoid of substance. I personally don’t have a lot of time to read other blogs, so when I do, I want to sink my teeth into something substantial that I can learn something from – not some “quick hit” post that skims the surface of a topic to get a pageview.

When I am hungry for information and knowledge, I want a 7-course meal of thought – I’m not there for appetizers, and I’m never on an idea diet.

I know I may be in the minority on this issue, and I am 100% comfortable with that.

I don’t write to cater to the casual reader looking to skim over a topic or concept – if my long posts drive some people away, I actually think that’s a good thing. There are plenty of sourcing and recruiting blogs for readers to choose from. I write for people who are looking for 7-course meals of sourcing and recruiting substance.

Speaking of sourcing and recruiting substance, in 2012 I plan on going deeper into some topics that I am very interested in and passionate about, but haven’t written about as much as I would have liked in 2011. For example, semantic search, the critical importance of data and technology in support of talent identification and acquisition, and applying Lean as well as proven supply chain principles to talent acquisition

In 2012, I will also continue to update my free sourcing and recruiting resources page, and you can continue to rely on my average of 1 post per week, typically published on Monday morning, and 1000+ words per post.

What I’d Like From You

As a reader of my blog, I’d like to hear from you. Specifically, I am looking to know:

• What would you like to see from me in 2012? Is there something you’d like to see me write more about? Please be as specific on content and topics as possible.
• Would you like to write a post on Boolean Black Belt? Although I’d never really thought about it before, I’d love to start having guest bloggers. If you have something helpful, insightful, though-provoking and interesting regarding recruiting, sourcing and/or social media that you’d like to share, posting it on Boolean Black Belt will get your content exposure to recruiting and sourcing practitioners and leaders all over the world. With nearly 4,000 subscribers and over 10,000 unique visitors per month from over 100 countries, I can and want to help you share your ideas with the global recruiting and sourcing community, including some of the best and brightest from the most well-respected companies and talent acquisition teams in the world.
• On a different note – what do you think about me changing the name of my site to something other than Boolean Black Belt? I write on so much more than Boolean search that I think “Boolean Black Belt” is too narrow and limited and doesn’t accurately reflect the totality of my content. I have some ideas in mind, but would honestly appreciate your input on my blog’s name and the idea of changing it to something else.

I’m looking forward to your input.

Once again, thank you very much for reading, sharing, and recommending my site and content, and you have my best wishes for a successful, fulfilling, and prosperous 2012.

Happy hunting!

The trouble with search today is that people put too much trust in search engines – online, resume, social, or otherwise.

I can certainly understand and appreciate why people and companies would want to try and create search engines and solutions that “do the work for you,” but unfortunately the “work” being referenced here is thinking.

I read an article by Clive Thompson in Wired magazine the other day titled, “Why Johnny Can’t Search,” and the author opens up with the common assumption that young people tend to be tech-savvy.

Interestingly, although Generation Z is also known as the “Internet Generation” and is comprised of “digital natives,” they apparently aren’t very good at online search.

The article cites a few studies, including one in which a group of college students were asked to use Google to look up the answers to a handful of questions. The researchers found that the students tended to rely on the top results.

Then the researchers changed the order of the results for some of the students in the experiment.  More often than not, they still went with the (falsely) top-ranked pages.

The professor who ran the experiment concluded that “students aren’t assessing information sources on their own merit—they’re putting too much trust in the machine.”

I believe that the vast majority of people put too much trust in the machine – whether it be Google, LinkedIn, Monster, or their ATS.

Trusting top search results certainly isn’t limited to Gen Z – I believe it is a much more widespread issue, which is only exacerbated by “intelligent” search engines and applications using semantic search and NLP that lull searchers into the false sense of security that the search engine “knows” what they’re looking for.

This is Your Search Without a Brain

It’s easy to see why people and companies create search products and services using semantic search and NLP that claim to be able to make searching “easier” – they are looking to sell a product  based on the value of making your life easier, at least when it comes to finding stuff.

If you take a look at some of the marketing materials for intelligent search and match search products, you’ll find value propositions such as “Stop wasting time trying to create difficult and complex Boolean search strings,” ”Let intelligent search and match applications do the work for you,” and “A single query will give you the results you need – no more re-querying, no more waste of time!”

I love saving time and getting to what I want faster, but my significant issue with “intelligent search and match” applications is that they try to determine what’s relevant to me.

And that’s a rather large issue, because only I know what I am looking for.

It’s critical to be reminded that the definition of ‘relevance,’ specifically with regard to information science and information retrieval, is “how well a retrieved document or set of documents meets the information need of the user.”

The only person that can make the judgment of how well a search result meets their information need is the person conducting the search, because it’s their specific information need.

Any reference to “relevance” by a search engine, whether it be Google, Bing, LinkedIn, Monster, etc., is based purely on the keywords, operators, and/or facets used.

Search engines don’t know what you want – they only know what you typed into or selected from the search interface.

Poor use of keywords, operators or facets will don’t stop you from getting results. All searches “work,” as I am fond of saying – but the quality or relevance will likely be low.

Of course, that assumes that the person conducting the search is actually proficient at judging the quality or the relevance of the results – comparing results to their specific information need and experimenting with different combinations of keywords, operators and facets to look for changes in relevance.

Related Does Not Equal Relevant

I personally never implicitly trust first page or top ranked search results online, nor top ranked results on LinkedIn, Monster, or anywhere I search. Some of the best search results I have ever found were buried deep in result sets – far past where most people would typically review, and essentially in the territory of results the search engine deemed least “relevant.” Pshaw!

One reason for this is because I understand that any search engine I use, no matter how “dumb” (straight keyword matching), or “intelligent” (semantic/NLP), they can only work with the terms I give it. What do you think the  most “intelligent” search engine can do with poor user input?

When it comes to searching, unfortunately everyone’s a winner, because every search “works” and returns results.  The problem is that few searchers know how to critically examine search results for relevance.

Regardless how how “intelligent” a search engine might be, it can only try to find terms and concepts related to my user input.

This is an often overlooked but critical issue – just because terms might be related, it does not mean they are relevant to my information need.

It certainly helps to understand that some of the most relevant search results can’t actually be retrieved by the obvious keywords, titles or phrases, or even those that a semantic search algorithm deems related to them. In fact, some of the best results simply cannot be directly retrieved – see my post on Dark Matter for more information on the concept.

However, to appreciate the concept that no single search, no matter how enhanced by technology, can find all of the relevant (by human standards and judgment) results available to be retrieved, you have to know a thing or two about information retrieval in the first place.

And if you already lack the ability to critically judge search results and evaluate them for relevance, how can you be expected to be able to evaluate and critically examine the search results returned by intelligent search and match applications?

The “black box” matching algorithms of intelligent search and match applications pose significant issues to users in that searchers have absolutely no insight as to why the search engine returns the results it does. Without this, what option does a user have other than to implicitly trust the search engine’s matching algorithm?

Searching Ain’t Easy

Who says search has to be easy anyway?

Just because you might want it to be, should it be? Does it have to be?

Let’s face it – a lot of people look for the easy way out. The sheer volume of advertisements pushing diet supplements that claim you can lose a ton of weight without having to watch what you eat and exercise is evidence that people want to get the results they want without working for them.

You know the best way to lose weight? A healthy diet combined with regular exercise. The problem is that eating healthy and exercising regularly is that it requires discipline and hard work.

I’m not saying there isn’t a better way to search – I am a fan of Thomas Edison’s belief that “There is always a better way.”

However, I believe that the better way, specifically when it comes to information retrieval, involves discipline and the hard work of people using critical thought in the search process – not short-cutting or completely removing it from the equation.

And I am not alone.

There is already considerable work being done to create new kinds of search systems that depend on continuous human control of the search process. It’s called Human-Computer Information Retrieval (HCIR) - which is the study of information retrieval techniques that bring human intelligence into the search process.

Truly intelligent search systems should not involve limiting or removing human thought, analysis, and influence from the search process – in fact, they should and can involve and encourage user influence.

When you break it down, the information retrieval process has 2 basic parts:

1. The user enters a query, which is a formal statement of their information need
2. The search engine returns results

The key, in my opinion, is that the search engine should return results in a ”Is this what you were looking for?” manner and allow you to intelligently refine your results, as opposed to a “This is what you were looking for” manner.

There’s a BIG difference.

The former begs for user influence and input, the latter does not – it makes the assumption that it found what you wanted

The bottom line is that no matter what you are using to search for information, only you know what you’re looking for and therefore judge the relevance of the search results returned.

Intelligent search isn’t easy, because you actually have to think before and after hitting the search button.

The Intelligent Search Process

As I have written before, searching should not be a once-and-done affair – there is no mythical “once search to find them all.”

Searching is ideally an iterative process that requires intelligent user input.

Here is an example of an intelligent, iterative search process applied to sourcing talent:

1. Analyzing, understanding, and interpreting job opening/position requirements
2. Taking that understanding and intelligently selecting titles, skills, technologies, companies, responsibilities, terms, etc. to include (or purposefully exclude!) in a query employing appropriate Boolean operators and/or facets and query modifiers
3. Critically reviewing the results of the initial search to assess relevance as well as scanning the results for additional and alternate relevant search terms, phrases, and companies
4. Based upon the observed relevance of and intel gained from the search results, modifying the search string appropriately and running it again
5. Repeat steps 3 and 4 until an acceptably large volume of highly relevant results is achieved

Anyone can enter search terms and hit the “search” button, but not everyone can effectively and intelligently search.

Until you’ve witnessed intelligent and iterative search in action, you likely wouldn’t know the difference between “great” search results, “good” search results and “bad” search results.

It’s as dramatic as the difference between and experienced professional offshore fisher, a recreational fisher, and someone going offshore fishing for the first time.

The ocean holds the same fish for everyone fishing it. While a first-time or recreational fisher can get lucky every once in a while, only a person who really knows what they’re doing can get “lucky” on a consistent basis and catch the fish  the recreational fisher only dreams of catching.

Final Thoughts

The ability to enter in some search terms and click the “search” button doesn’t convey any supernatural search ability, but it does certainly make people feel like they are good at searching, because unless you mistype something, everyone’s a winner.

Ultimately, search engines of all types retrieve information, but information requires analysis, and only humans can analyze and interpret for relevance.

Eiji Toyoda, the former President of Toyota Motor Corp., has observed that “Society has reached the point where one can push a button and immediately be 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.”

Critical thinking is perhaps the most important skill a knowledge worker can possess.

The reason why so many people stink at search is because most people simply don’t think before or after they search, and they place too much trust in the machine.

Additionally, the quality of the search terms/info entered directly affects the quality of the results. “Garbage in = garbage out” certainly applies here. And effective searching is rarely a “once and done” affair – the ability to critically evaluate search results for relevance and successively refine the search criteria to increase relevance is the key to true “intelligent search.”

“Black box” matching algorithms can be wonders of technology and engineering, but they pose significant problems in that searchers have absolutely no insight as to why they return the results they do, and in many cases, the engineers creating these semantic/NLP matching algorithms assume they know what their users are looking for better than the users themselves. I’m sorry if I am the only person offended by such an assumption.

Okay, I’m not sorry.

I love technology, and I use and have used some of the best matching technology available, but also I know it’s not a good idea to try to limit or remove intelligent critical thinking from the search process and completely replace it with matching algorithms.

The term human–computer information retrieval was coined by Gary Marchionini whose main thesis is that “HCIR aims to empower people to explore large-scale information bases but demands that people also take responsibility for this control by expending cognitive and physical energy.” (emphasis mine)

For those who simply want information systems to magically provide them with the most relevant results at the click of a button, you should take special note of the fact that experts in the field of HCIR do not believe that people should step out of the information retrieval process and let semantic search/NLP algorithms/AI be solely responsible for the search process.

If you want to get better search results, use the latest technologies, but don’t put too much trust in the machine.

Instead, put some skin in the game, take responsibility for the search process, and expend some cognitive energy critically thinking through not only your search input, but also the results for relevance.

“In the age of information sciences, the most valuable asset is knowledge, which is a creation of human imagination and creativity. We were among the last to comprehend this truth and we will be paying for this oversight for many years to come.” — Mikhail Gorbachev, 1990

Strictly For the Search Geeks

Check out this HCIR Challenge, and at least read the  introduction which compares and contrasts precision vs. recall, and references iterative query refinement.

Have you ever had to find bilingual candidates or search for people who speak a specific language?

If so, you’re in luck – this week’s post focuses on a few ways of how do just that.

If not, no worries – the thought processes and search strategies I use in this post are helpful no matter what it is you’re searching for, because searching is 95% critical thinking and 5% Boolean syntax.

Boolean Search for Explicit Bilinguals

As with all searches, you must first realize that not all people who can speak more than one language will actually make mention of that in their resume, their LinkedIn/Google+/Facebook/Twitter profile or updates, or anywhere else they may leave information about themselves online. I’ll show you at least 1 way of searching for people who are highly likely to be bilingual, but do not make any explicit mention of languages or polyglotism later.

To find those that do make explicit mention of their ability to speak multiple languages, you can of course search for specific mention of the language you’re searching for. Here is the most basic and obvious example that anyone would think of:

• AND Spanish

However, while this will yield some people who are bilingual in Spanish, it will also yield false positives in many cases – pulling up people who took courses in high school or college, and people who make mention of Spanish because they have basic speaking or reading/writing skills. In most cases, employers looking for bilingual people are looking for people who are fully fluent.

Before I get to any more examples, I have to let you know I always hesitate to provide sample search strings because Booelan searches should be bespoke – specifically tailored to meet the precise information need.

And you must realize there is never a perfect/ultimate Boolean search string to find anything – all searches “work” provided they are syntactically correct, and practically any search can (and should!) be iteratively improved upon through successive searches for ever-improving relevance and results.

Also, although some people have a hard time wrapping their heads around this fact, every search you run both includes some people with what you’re looking for and excludes some people who have what you’re looking for, so no single search can possibly find all potential matches.

Now that you’ve read and accepted my Boolean search dislaimer, let’s take a look at a few more search string examples.

Let’s say you needed to find someone that was bilingual in Spanish. With any search engine/site offering basic Boolean, you could using something like this:

• __________ AND Spanish AND (Native OR fluent OR bilingual OR “bi-lingual”)

If you had access to Monster, you could leverage fixed proximity search to force the mentions of “Spanish” and Native OR fluent OR bilingual OR “bi-lingual” to be within 10 words of each other, increasing the likelihood that they are mentioned in the same sentence, which has extremely high semantic value.

• __________ AND Spanish NEAR (Native OR fluent OR bilingual OR “bi-lingual”)

Bing supports configurable proximity with its NEAR:X functionality, enabling you to tighten up the distance between the mention of a specific language and fluency. Here’s a few basic examples searching LinkedIn via Bing (I don’t combine them into one search because Bing’s NEAR:X doesn’t play well with OR statements):

• site:linkedin.com Spanish NEAR:3 bilingual
• site:linkedin.com Spanish NEAR:3 native
• site:linkedin.com Spanish NEAR:3 fluent

That works for text in LinkedIn profiles…

…as well as information entered into the languages section in LinkedIn profiles:

As you can see in the above example, however, Bing’s proximity works bidirectionally (forwards and backwards), so you may find people who mention that they are bilingual/native with another language other than your target language, and something less than native/bilingual proficiency with your target proficiency.

For example:

Leveraging Google’s 1 or more word wildcard asterisk, you could target the specific word order to compensate for Bing’s bidirectional proximity:

• site:linkedin.com “spanish * native” “location * greater new york city” -dir

Even controlling the order of the terms isn’t infallible, given that Google asterisk is a 1 or more wildcard, which allows Google to “skip” across 1 or more terms. In the 4th result from the search above, Google skips across the target language to another, as can be seen below in the Skills section of LinkedIn:

Granted, the person above claims to be fluent in Spanish and native in Portuguese, but you can see how Google “skipped” across the words “fluent” and “Portuguese” to connect “Spanish” with “native.” Because Google’s asterisk modifier is imprecise, it can and will at times yield imprecise search results.

Moving away from LinkedIn, you can use Google’s asterisk when searching for online resumes quite effectively. For example, you could start with something like this:

• __________ “bilingual * spanish”

Here is a basic example of a Google search for resumes of people who mention being bilingual in Spanish and also mention Java.

It works.

Of course, you can also experiment with the number of asterisks you put between “bilingual” and the language you’re searching for.

And for all of the above searches, you can and should mix and match a variety of terms that can be used to describe a high level of language proficiency (bilingual, bi-lingual, native, fluent, etc.), as well as any/all of the ways a person might refer to a language (e.g., Chinese, Mandarin, Cantonese).

LinkedIn Language Searching

LinkedIn has built-in language search in their advanced search interface which ties into the languages section on LinkedIn profiles.

One of the limitations of using LinkedIn’s language search functionality is that not everyone will actually use the languages section on their profile – a person needs to consciously make the effort to add the languages section to their profile and then choose their proficiency.

Which leads me to the next limitation. As you can see above, you can search for a language, but you can’t search by proficiency level.

Here is the list of proficiency levels a person can choose from when adding a language to their LinkedIn profile:

I love the fact that LinkedIn included languages to profiles, but they left many unanswered questions. For example – what’s the real difference between “Full professional proficiency” and “Native or bilingual?” Would someone choose “Professional working proficiency” if they were fluent? What level of fluency could that represent?

It would also be nice if you could search by fluency level, but even if you could, there would have to be some kind of definition/description of what each level is supposed to represent so people could intelligently and accurately select their own proficiency, as well as enable others to more accurately search by proficiency level.

When I searched for Java developers and selected “German” in the language facet, the top result was of someone who only had “limited working proficiency.” Not too helpful if I needed someone who was fluent.

LinkedIn Language Search: Skills

As I showed above with my example Google X-Ray search of LinkedIn, some people enter languages in their Skills section.

Here’s another example:

You can search for language skills in LinkedIn – here’s a Spanish example:

I love the Skills concept, but:

1. It’s still in beta
2. Not everyone uses them
3. They don’t appear to be searchable in conjunction with anything else (from inside of LinkedIn)
4. People can enter non standardized Skills

For example, this person entered a Skill of “Spanish – fluent.”

If you click on “Spanish – fluent,” this is what you get:

Boolean Search for Implicit Bilinguals

So how do you go about finding people who may be bilingual but don’t mention any languages on their resume/profile?

One way is through implicit search, which is the 3rd level of Talent Mining.

For example, try this search on LinkedIn, searching only the United States:

• Title: engineer
• Keywords: (Beijing OR Shanghai OR “Republic of China” OR Putonghua OR Guoyu OR Harbin OR Xian OR “Xi’an” OR Nanjing OR Chongqing)

Here is what the first page of results looks like for me:

If you click on the first result for me (sorted by Relevance), you will see that the profile shows that they have been working in the United States as early as 1999, and here is their education section:

Do you think they might be bilingual in English and Chinese? You’ll also notice there is no mention of any language, nor of being bilingual.

Taking this approach one step further, you should ensure that your searches are mutually exclusive of explicit bilinguals. To do so, simply NOT out the obvious search terms.

For example:

• -(Chinese OR Mandarin OR Cantonese)

If you modify my first example search above to make it mutually exclusive, here is what it would look like:

• Title: engineer
• Keywords: -(Chinese OR Mandarin OR Cantonese) (Beijing OR Shanghai OR “Republic of China” OR Putonghua OR Guoyu OR Harbin OR Xian OR “Xi’an” OR Nanjing OR Chongqing)

Click here to see the search results – there are 5,218, which isn’t much less than the 6,019 from the first search above. That shows that this technique definitely uncovers a solid amount of people who have a high probability of being bilingual in my target language who do not make any explicit mention of a language or polyglotism.

Now, the keywords I used in the example are FAR from exhaustive or complete – you could easily build it out to include most of the major universities in China, as well as all of the provinces and major cities. The first result in my search already gave me a new term to add to my search – Tsinghua.

This same implicit search approach could be used for any language.

However, please note that this is something that you can do after you’ve already searched for and found all of the people you can from the direct searching methods that you would typically perform first, including the ones I detailed in this post.

While this kind of implicit search can certainly yield false positive results, it taps into Dark Matter profiles (and resumes!) and is guaranteed to find you people who are bilingual that you CANNOT find through any direct search methodology.

Have Some Great Bilingual Search Techniques?

Now that I’ve given you a few ways you can try to find people who are likely to be bilingual, what are your favorite techniques for identifying bilingual candidates?

Take this chance to share with the international sourcing and recruiting community!

A Bit of Bilingual Search Humor…

Of course, when searching for bilingual professionals, it helps if they spell the language correctly.

Have you ever used Microsoft Word to build and save Boolean search strings to be able to copy and paste entire searches into LinkedIn, Monster, Dice, or your ATS ?

I did for quite some time, until I ran into a problem.

Many years ago, I noticed that several sites, including Monster, Dice, and LinkedIn were not “obeying” my request for exact phrases in my searches.

If I searched for “project manager,” I would get results in which “project” and “manager” were highlighted as keyword hits even though they were mentioned separately and not together as a phrase.

One day I contacted Dice to ask what was going on with my inability to retrieve exact phrases using quotation marks and the support rep asked me what I was using to build my search strings. I told her I was using Microsoft Word to build and save my strings and that I would copy and paste them into Dice, to which she responded with, “There’s your problem.”

She summarily explained to me that Dice doesn’t recognize Microsoft Word’s default quotation mark format (aka “smart quotes”). Turns out that Monster, LinkedIn, and many others don’t recognize “smart quotes” for exact phrase searching either.

Here’s an example of Word’s “smart quotes:”

She then suggested that I either enter my queries directly into Dice or use Notepad to build and save my Boolean searches because they produce “straight quotes.”

Well, I didn’t want to build my searches in Dice’s search field because I could not see my entire search string as I was building it.

This is an issue with just about any job board resume database, ATS, Internet search engine, and social network – the search fields are all ridiculously short (e.g., LinkedIn’s is 37 characters).

Oh, and if you need evidence that LinkedIn does not actually recognize Word’s smart quotes for exact phrase searching, try this search on LinkedIn:

After my call with the Dice support rep, I started using Notepad to build and save my searches so I could simply copy and paste them directly into any job board resume database, Internet search engine, social network, and my ATS and they would recognize and obey the quotation marks as a request for an exact phrase.

However, it turns out there is a way to force Microsoft Word to not use “smart quotes” and instead use “straight quotes” – which most sites, social networks, databases, and search engines will obey as a request for an exact phrase.

How to Create Straight Quotation Marks in Microsoft Word

First, go to File –> Options.

Then go to Proofing and click on AutoCorrect Options.

Once in the AutoCorrect options, choose the “AutoFormat As You Type” tab, and then ensure that the “Replace as you type – ‘Straight quotes’ with ‘smart quotes’” is NOT checked/selected.

From then on, whenever you type quotation marks, they will be “straight quotes,” which will work for searching for exact phrases on any site or system that allows for exact phrase searching via quotation marks.

This also works for MS PowerPoint, for those of you who may copy and paste Boolean search examples from a presentation from time to time.

How to Fix an Entire Library of Boolean Searches in MS Word

If you happen to have a decent amount of Boolean search strings saved in Word and you didn’t notice the difference between smart quotes and straight quotes, you can use Word’s Find and Replace functionality to quickly convert all smart quotes into straight quotes.

Simply invoke Word’s Find and Replace and enter each smart quote in the “Find what” field and a straight quote in the “Replace with” field, and then click “Replace All.” You’ll have to do this with the leading smart quote and the ending smart quote, as they are different.

Saving Your Boolean Search Strings – Beyond Word

If you don’t want to use Word for building, saving, and copying/pasting Boolean search strings, you can use Notepad.

You can also use Excel if you like, as Excel doesn’t use smart quotes. Excel has the added benefit of allowing you to automatically build large Boolean OR statements as well.

Another option is Google Docs, which allows for the ability to share and collaborate with a library of Boolean searches online with others. However, by default, Google Docs also uses smart quotes.

To switch from smart quotes to straight quotes in Google Docs, navigate to Tools –> Preferences:

In the Preferences menu, be sure to deselect “Use smart quotes.”

Happy hunting!

I was recently performing some searches on Bing and came across something curious that I had never noticed before.

I’m not exactly sure if what I found is new or simply something I’ve overlooked in the past. I updated Twitter with “Did you know that Bing supports the + query modifier?” on November 10th, wondering if it was something that other people knew about.

I only received a few responses, including a couple from noted sourcing luminaries, and the consensus was that I didn’t find anything because it wasn’t documented anywhere and they could not get it to work.

However, the +/Plus sign does in fact work when searching Bing – just not like it used to in Google.

It’s always a little exciting to think you are one of the first people to stumble across something most people don’t know about, although I won’t get my hopes up that I’m the only person outside of some folks at Microsoft who’s ever figured out that Bing supports the +/Plus sign in searches.

This discovery also led me to proof of Bing leveraging semantic and phonetic search. 

Bing Search Supports the +/Plus Sign

So I was tinkering around on Bing testing very basic LinkedIn X-Ray searches (more on that later), and here’s my original Bing search of LinkedIn: site:linkedin.com “location Houston” java

Here are the results I found – notice anything odd?

I immediately noticed that the 3rd, 4th, and 5th results highlighted keyword hits of “Coffee.”

My first response was confusion – I could not recall Bing ever trying to so obviously perform semantic search and attempt to guess what I might be looking for by returning results with related terms I didn’t actually search for.

Then I scanned back up the page and noticed something similar to what I see on Google all the time, typically when Google thinks I might have misspelled something:

When I clicked on “Do you want results for site:linkedin.com “location Houston java,” this is what I saw:

The first thing I noticed was the +/Plus sign.

I could not recall ever seeing it before when searching Bing.

Then I looked at the results, and it was obvious that the +/Plus sign was serving to remove Bing’s attempt at semantic search and only return results with the exact terms I searched for.

No more results mentioning “coffee” when I was searching for Java.

If you think my observation of the +/Plus sign was a fluke, the very next day I was helping one of my associates with a search and noticed he used “HSCM” in an OR statement for a PeopleSoft FSCM position. I had never encountered HSCM before on a resume making reference to anything PeopleSoft SCM related, so I Binged it.

My search was simply PeopleSoft HSCM.

When I saw the results, I noticed the “Including results for peoplesoft hcm,” even though I searched for HSCM.

In this case, I don’t think Bing was trying to perform semantic search and return a related search term – I think Bing was actually steering me towards a spelling variant that is more common to Bing’s index, perhaps assuming that I misspelled the term in my original search.

When I clicked on “Do you want results for PeopleSoft HSCM,” there were only 31 results, and the +/Plus sign was there, preceding the search string:

If you try the same search on Google, Google doesn’t give you the benefit of the doubt and simply assumes you misspelled your search term and gives you results for what Google assumes you were searching for.

How rude.

I know it’s a stretch, but there are some people who actually do know what they are searching for and would rather not have their searches hijacked.

Bing vs. Google

I was a very early adpoter of Google’s search engine (think 1998), preferring it over what most “power searchers” were using back then (think AltaVista).

For many years I was a Google extremist – I used Google search for literally all of my searching needs and never bothered to search using any other Internet search engine except for experimental poking around.

However, not too long ago, after getting frustrated with the junk Google was returning in my LinkedIn searches as well as Google more frequently questioning my humanity by forcing me to jump through CAPTCHA hoops , my experimental poking around with Bing got more serious.

At this time, I use Bing more than I use Google – I’d estimate a 60/40 split.

Part of this is driven by the fact that I find Bing X-Ray searches of LinkedIn are so much “cleaner” and not subject to as much “noise” as Google search results. I also find searching for LinkedIn profile headline phrases in Bing to do a very good job of returning the profile I’m looking for, even if I don’t use the site: command to specifically search LinkedIn.

And of course I love the fact that Bing supports configurable proximity with the NEAR:X search functionality, allowing me to perform feats of magic and semantic search at the sentence level.

I also like the fact that, as I showed above, Bing will by default include your search terms along with results of terms it thinks you might find relevant.

With similar searches, Google just assumes you don’t really know what you were searching for and gives you results of what it thinks you were searching for.

And if you happen to be searching for flights, Bing’s Price Predictor totally rocks!

Unrelated to sourcing and recruiting, I know – but a gem nonetheless!

Bing Searchers Beware of Semantic and Phonetic Search

Now that I am on the lookout for Bing’s semantic search, I’ve noticed that sometimes Bing will slip in semantic search results without giving you the “Including results for ____ / Do you want results for _____” heads-up that lets you know Bing has included results with terms you didn’t actually search for that Bing thinks is related and relevant.

For example, here are the first page search results for a Java search that returns “Coffee” and more interestingly “Coffey” – which means that Bing is not only going semantic by returning words that may have a similar meaning in certain contexts, but also phonetic, returning words that sound similar to the search term.

If you explore the cached page for the Coffey result, you will notice that there isn’t any mention of Java anywhere, so the only thing I can conclude is that Bing took my search term of Java and leveraged semantics to also search for coffee as well as phonetic variants, such as Coffey.

I know there have to be a few fellow search geeks that find that prospect to be quite interesting. It looks like the folks behind Bing search have been busy!

In any event, the real lesson here is that Bing didn’t give me a heads-up that it decided to also return results with terms I didn’t actually search for.

So, if you’re using Bing to search for anything and you don’t want it taking any liberties with semantic search because you only want results with the exact search terms you used, be sure to add a +/Plus sign to the beginning of your search, like so: