Category Archives: Extended Boolean

Using Extended Boolean to Achieve Semantic Search in Sourcing

When it comes to sourcing and recruiting, semantic search is perhaps the most powerful way to quickly find people who have experience you’re looking for.

Now, I am not talking about black box semantic search (e.g., Google, Monster’s 6Sense, etc.).

I’m referring to user-defined semantic search, where you tell a search engine exactly what you want with your query, and the search engine doesn’t try to “understand” your search terms or “figure out” what you mean through taxonomiesRDFa, keyword to concept mapping, graph patterns, entity extraction, fuzzy logic, etc.

If you’re not very familiar with semantic search (for sourcing – not search engines), I strongly suggest you read my comprehensive article from January 2012 on the subject: The Guide to Semantic Search for Sourcing and Recruiting. Continue reading

Monster’s Undocumented Boolean Search Operators & Query Compression

 

Monster logo smallThe other week I came across a question regarding Monster’s search operators in the Boolean Strings group on LinkedIn and I realized that most people don’t know that Monster’s classic resume search has a few undocumented search operators as well as powerful semantic search capability.

In this article I will detail two of Monster’s undocumented search operators, how to compress your Boolean search strings by more than 30%, and remind you of Monster’s documented but seldom used NEAR search operator.

AND = & + OR = |

Although I can’t seem to find any documentation of it, Monster’s search functionality does support the & for the Boolean AND search operator as well as | for OR Boolean search operator – which can save on character space for longer queries.

While most people don’t run searches that will test Monster’s main search field limit of 500 total characters (including spaces), there are those sourcers and recruiters who extensively leverage conceptual search, employing comprehensive OR statements for each concept in their Boolean search string, which can easily exceed 500 characters, especially when searching for a number of target companies.

In cases such as these, it can be helpful to use the ampersand (&) for AND and the pipe symbol (|) for OR, effectively cutting the number of characters used for AND’s and OR’s by 60% (5 total characters down to 2).

For example, compare these two searches which return the exact same results:

  • iOS AND (ObjectiveC OR “Objective-C”) AND (cocoa OR xcode) AND (iPhone* OR iPad*) AND (“apple store” OR iTunes OR “app store”) AND (SQL* OR xib)
  • iOS & (ObjectiveC | “Objective-C”) & (cocoa | xcode) & (iPhone* | iPad*) & (“apple store” | iTunes | “app store”) & (SQL* | xib)

Even with a relatively short Boolean search string of 144 characters, you can save over 10% by using & and | (128 vs. 144 characters).

If you wanted to compress your queries further, you can actually eliminate all spaces in your Boolean search string with no negative effects.

For example – this Boolean search string returns the exact same results as the above two searches:

  • iOS&(ObjectiveC|”Objective-C”)&(cocoa|xcode)&(iPhone*|iPad*)&(“apple store”|iTunes|”app store”)&(SQL*|xib)

Sadly, Monster does not support the minus sign (-) for the NOT operator.

However, you do not have to type AND NOT, nor & NOT – a simple NOT will do.

In fact, you don’t even have to capitalize NOT or any other Boolean search operator, for that matter – lowercase not works exactly the same.

Thanks Monster!

Boolean Search: Who Needs AND Anyway?

Interestingly, most people also don’t know that you don’t have to type AND or & – similar to LinkedIn, Google, Bing, etc., any space can be an implied AND.

For example, this search runs exactly as the ones above:

  • iOS (ObjectiveC|”Objective-C”) (cocoa|xcode) (iPhone*|iPad*) (“apple store”|iTunes|”app store”) (SQL*|xib)

Furthermore, you don’t even have to use a space to leverage implied AND functionality – this search returns the exact same results:

  • iOS(ObjectiveC|”Objective-C”)(cocoa|xcode)(iPhone*|iPad*)(“apple store”|iTunes|”app store”)(SQL*|xib)

Now we’re down to 101 characters, which is nearly 30% more efficient than our original 144 character search.

How’s that for Boolean search efficiency?

If you’re wondering how I figured this stuff out, it’s actually quite simple – curiosity and experimentation.

I challenge you to be curious and to experiment – from time to time, simply ask, “I wonder what would happen if…..?” and give something a try.

Hopefully all of what I’ve shared with you today has made you curious about your other sources and how you might be able to experiment and tweak your searches for other sites to make discoveries and yield additional benefits.

If you you do – please let me know!

Monster’s NEAR Operator: Documented but Seldom Used

Although Monster’s extended Boolean NEAR search operator is documented, most people don’t use it. This is unfortunate, because proximity search is incredibly powerful and can help you zero-in on people based on what they’ve actually done vs. resumes containing search keywords.

Monster’s NEAR operator is an example of fixed proximity search, which can be used to return results with words, phrases or OR statements within 10 words of other words/phrases, or OR statements, which can enable semantic search at the sentence level.

Would you be interested in learning more about sentence level semantic search using the NEAR operator?

 

100+ Free Sourcing & Recruiting Tools, Guides, and Resources

 

It’s been a LONG time coming, but I finally got around to updating my free sourcing & recruiting tools, guides and resources page where I now keep a current list of the best of my work all in one place for easy bookmarking and reference.

You can find it here on my main page:

 

Here is where you can find all of the best of my Boolean Black belt content all in one place - free sourcing and recruiting how-to guides, tools, presentations, and videos - be sure to bookmark it, and if you're feeling  friendly, tweet it, share it on LinkedIn and/or +1 it on Google Plus.  Many thanks!

 

Additionally, I thought I might as well put all of my best work all in one blog post as well – over 110 of my articles in one place for easy referencing!

My blog is a pursuit of passion and not of profit – if you’ve ever found anything I’ve written helpful to you, all I ask is that you tweet this out, share it on LinkedIn, like it on Facebook, or give this a +1 on Google.

Many thanks for your readership and support – please pay it forward to someone who can benefit.

Big Data, Analytics and Moneyball Recruiting

Big Data, Data Science and Moneyball Recruiting

The Moneyball Recruiting Opportunity: Analytics and Big Data

Human Capital Data is Sexy – and Sourcing is the Sexiest job in HR/Recruiting! 

Is Sourcing Dead? No! Here’s the Future of Sourcing

The End of Sourcing 1.0 and the Evolution of Sourcing 2.0

How to Find Email Addresses

How to Use Gmail and Rapportive to Find Almost Anyone’s Email Address

Social Discovery

2 Very Cool and Free Social Discovery Tools: Falcon and TalentBin

Talent Communities

The Often Overlooked Problem with Talent Communities

Lean / Just-In-Time Recruiting / Talent Pipelines

What is Lean, Just-In-Time Recruiting?

Lean Recruiting & Just-In-Time Talent Acquisition Part 1

Lean Recruiting & Just-In-Time Talent Acquisition Part 2

Lean Recruiting & Just-In-Time Talent Acquisition Part 3

Lean Recruiting & Just-In-Time Talent Acquisition Part 4

The Passive Candidate Pipeline Problem

Semantic Search

What is Semantic Search and How Can it Be Used for Sourcing and Recruiting?

Sourcing and Search: Man vs. Machine/Artificial Intelligence – My SourceCon Keynote

Why Sourcers Won’t Be Replaced By Watson/Machine Learning Algorithms Any Time Soon

Diversity Sourcing

How to Perform Diversity Sourcing on LinkedIn – Including Specific Boolean Search Strings

How to Use Facebook’s Graph Search for Diversity Sourcing

Social Recruiting

How to Find People to Recruit on Twitter using Followerwonk & Google + Bing X-Ray Search

Google Plus Search Guide: How to Search and Find People on Google Plus

Facebook’s Graph Search Makes it Ridiculously Easy to Find Anyone

How to Effectively Source Talent on Social Networks – It Requires Non-Standard Search Terms!

How a Recruiter Made 3 Hires on Twitter in Six Weeks!

Twitter 101 for Sourcers and Recruiters

Anti-Social Recruiting

How Social Recruiting has NOT Changed Recruiting

Social Recruiting – Beyond the Hype

What Social Recruiting is NOT

Sourcing Social Media Requires Outside the Box Thinking

Social Networking Sites vs. Job Boards

LinkedIn Sourcing and Recruiting

Sourcing and Searching LinkedIn: Beyond the Basics – SourceCon Dallas 2012

LinkedIn’s Dark Matter – Profiles You Cannot Find

How to Get a Higher LinkedIn InMail Response Rate

The Most Effective Way to X-Ray Search LinkedIn

LinkedIn Catfish: Fake Profiles, Real People, or Just Fake Photos?

LinkedIn Search: Drive it Like you Stole It – 8 Minute Video of My LinkedIn Presentation in Toronto

How to Search LinkedIn and Control Years of Experience

How to Quickly and Effectively Grow Your LinkedIn Network

How to View the Full Profiles of our 3rd Degree Connections on LinkedIn for Free

How to Find and Identify Active Job Seekers on LinkedIn

LinkedIn Profile Search Engine Optimization

Free LinkedIn Profile Optimization and Job Seeker Advice

Do Recruiters Ruin LinkedIn?

The 50 Largest LinkedIn Groups

How to See Full Names of 3rd Degree LinkedIn Connections for Free

How I Search LinkedIn to Find People

LinkedIn’s Undocumented Search Operator

Does LinkedIn Offer Recruiters any Competitive Advantage?

Have You Analyzed the Value of Your LinkedIn Network?

Where Do YOU Rank In LinkedIn Search Results?

What is the Total Number of LinkedIn Members?

Beware When Searching LinkedIn By Company Name

LinkedIn Sourcing Challenge

How to Search for Top Students and GPA’s on LinkedIn

What’s the Best Way to Search LinkedIn for People in Specific Industries?

18 LinkedIn Apps, Tools and Resources

LinkedIn Search: What it Could be and Should be

How to Search Across Multiple Countries on LinkedIn

Private and Out of Network Search Results on LinkedIn

How to “Unlock” and view “Private” LinkedIn Profiles

Searching LinkedIn for Free – The Differences Between Internal and X-Ray Searching

Sourcing and Boolean Search

Basic Boolean Search Operators and Query Modifiers Explained

How to Find Resumes On the Internet with Google

Challenging Google Resume Search Assumptions

Don’t be a Sourcing Snob

The Top 15 Talent Sourcing Mistakes

Why Boolean Search is Such a Big Deal in Recruiting

How to Become a World Class Sourcer

Enough with the Exotic Sourcing Already – What’s Practical and What Works

Sourcing is So Much More than Tips, Tricks, Hacks, and Google

How to Find, Hire, Train, and Build a Sourcing Team – SourceCon 2013

How to Use Excel to Automatically Build Boolean Search Strings

The Current and Future State of Sourcing

Why So Many People Stink at Searching

Is your ATS a Black Hole or a Diamond Mine?

How to Find Bilingual Professionals with Boolean Search Strings

How to Best Use Resume Search Aggregators

How to Convert Quotation Marks in Microsoft Word for Boolean Search

Boolean Search, Referral Recruiting and Source of Hire

The Critical Factors Behind Sourcing ROI

What is a “Boolean Black Belt?”

Beyond Basic Boolean Search: Proximity and Weighting

Why Sourcing is Superior to Posting Jobs for Talent

The Future of Sourcing and Talent Identification

Sourcing is an Investigative and Iterative Process

Beyond Boolean Search: Human Capital Information Retrieval

Do you Speak Boolean?

Is Recruiting Top Talent Really Your Company’s Top Priority?

Sourcing is NOT an Entry Level Function

Boolean Search Beyond Google

The Internet Has Free Resumes. So What?

How to Search Spoke, Zoominfo and Jigsaw for Free

Job Boards vs. Social Networking Sites

What to Do if Google Thinks You’re Not Human: the Captcha

What if you only had One Source to Find Candidates?

Passive Recruiting is a Myth – It Doesn’t Exist

Sourcing: Separate Role or Integrated Function?

The #1 Mistake in Corporate Recruiting

How I Learned What I Know About Sourcing

Resumes Are Like Wine – They Get Better with Age!

Why Do So Many ATS Vendors Offer Such Poor Search Functionality?

Do Candidates Really Want a Relationship with their recruiter?

Recruiting: Art or Science?

What to Consider When Creating or Selecting Effective Sourcing Training – SourceCon NYC

The Sourcer’s Fallacy

Sourcing Challenge – Monster vs. Google – Round 1

Sourcing Challenge – Monster vs. Google – Round 2

Do You Have the Proper Perspective in Recruiting?

Are You a Clueless Recruiter?

Job Boards and Candidate Quality – Challenging Popular Assumptions

When it Comes to Sourcing – All Sources Are Not Created Equal

Boolean Search String Experiments

Boolean Search String Experiment #1

Boolean Search String Experiment #1 Follow Up

Boolean Search String Experiment #2

 

Talent Sourcing: Man vs. AI/Black Box Semantic Search

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
Enjoy, and let me know your thoughts.

What is a Boolean Black Belt Anyway?

I’ve been blogging nearly 3 years now, and I realized I’ve never come out and actually defined the term “Boolean Black Belt.”

The concept seems pretty self explanatory, but there has been at least 1 person who’s taken the opportunity to point out (and gain some traffic in the process – but it’s all good!) that it could be perceived as a bit of an oxymoron to be an “expert” in something as simple as 3 Boolean operators.

Interestingly, however, I’ve found that most sourcers and recruiters don’t even fully exploit the various powers of the OR and NOT operators – not even close.

So what is a “Boolean Black Belt” anyway? Continue reading

Beyond Boolean Search: Proximity and Weighting

Beyond Basic Boolean

Most sourcing, recruiting, and staffing professionals are familiar with the basic Boolean operators of AND, OR, and NOT. However, I have found that few are familiar with what some refer to as “extended” Boolean functionality, such as proximity search and term weighting.

Proximity and term weighting, where supported, are not actually logical (Boolean) operators – they are more accurately referred to as text or content operators.

Whatever you call them – extended Boolean or text operators – they offer sourcers and recruiters significantly more control, power and precision when executing searches, and in the hands of an expert, they can enable semantic search. Continue reading

The Big Deal about Bing for Sourcing and Recruiting

I’ve been a Google search fan for many years – since 1998, and I’ve used it exclusively for all of my search needs, both personal and professional.

Until recently.

That’s because I’ve discovered that Bing has a number of advantages over Google when it comes to sourcing candidates, including:

  • Cleaner, shorter, simpler and effective LinkedIn X-Ray searching
  • Effective Twitter X-Ray searching
  • Never doubting your humanity and refusing to run your more advanced queries
  • Configurable proximity (although I just learned Google has a similar capability)
  • Converting searches into RSS feeds Continue reading

LinkedIn Search: What it COULD and SHOULD be

Did you know that LinkedIn currently has the ability to deliver incredibly powerful search functionality to its users – WELL beyond what we all have access to now?  What am I talking about?

I’m excited to tell you, but quite honestly, I actually can’t believe it’s taken me this long to put 2 and 2 together. Have you ever really watched the video clip below that you can find on  LinkedIn’s Learning Center as well as on YouTube?

If you ignore the information regarding the new features and pay close attention to the video, you can hear Esteban talk about how LinkedIn is always on the lookout for talented Lucene Open Source engineers and watch him search for them. Lucene is an open source text search engine that I’ve written about in multiple posts for its advanced search functionality, including extended Boolean.

LinkedIn uses Lucene as their Text Search Engine

When I first watched the video, I never gave the Lucene stuff a second thought because LinkedIn doesn’t actually offer any of Lucene’s truly advanced search functionality – LinkedIn doesn’t even support root-word/wildcard searching, let alone extended Boolean search. I figured if they were already using Lucene for their text search engine they would offer all of Lucene’s search functionality, which they don’t.

Then I watched the video again the other day (not exactly sure why) and I it made me curious. Had they already implemented Lucene, or were they looking to do so? I did some research to see if I could confirm a link between LinkedIn with Lucene (pun intended).  Although TechCrunch reported that LinkedIn upgraded its people search, they failed to mention the technology behind the upgrade. I was then able to dig up an article that verified that LinkedIn had implemented Lucene as their text search engine.

So What Can LinkedIn Do With Lucene?

I’m glad you asked – be prepared to be amazed!  Continue reading