Digging into Semantics

Sourcers are only but as good as the information they find. So how can semantic searching help retrieve the best information? Very different than Boolean, which utilizes specific keywords, semantic takes contextual data and analyzes it. Artificial Intelligence has taken search to the next level of analysis and examines and interprets the meaning behind the words and concepts. With so many different human language variations, this would be the most effective way to find precise results.

Let’s examine semantic search further. Semantic search may recognize titles, keywords, must haves. It will offer recommendations for synonymous terminology, abbreviations, target companies, etc. Semantic search will also rank the results retrieved from most relevant to least based on how well they meet the concept, context, and synonyms of the search. You will get results that are relevant but do not include the keywords. Think about Glen Cathey’s “dark matter.”

So how do we find the “dark matter?” Let’s dig into the superhuman power of Linkedin’s semantic-based searching feature and compare boolean versus semantic.

Case Study:

Search is for a Software Developer that has experience in javascript, CSS, html, AND jquery living in the Washington DC area.

Boolean String: (“software developer” OR “software engineer”) javascript css html jquery

Results:  around 3,700

 

Semantic Selection:

Job title: Software Engineer, Software Developer, Frontend Developer, Web Developer, Frontend Engineer, Software Development Engineer, Frontend Engineer

Skills: Javascript, jquery, html, css

 

Results: around 14,400 results

 

Can you say huge dark matter? So why the significant difference, well it’s simple. The semantic search recognized synonyms of software developer and suggested them to me. I then, in turn, added them to my search which presented more results. I would rather have approximately 14,400 results where the data is ranked based on the significance of the candidate actually developing in their current job than near 3,700 results where the computer has scanned for keywords and the candidate hasn’t developed anything within the last few years.

 

In Layman’s Terms

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Boolean logic:  Natalya AND sourcing

Semantic logic: Natalya is sourcing

Every time I start a search I always recall Glen Cathey’s “Noun/Verb search, searching for what people have done.” This to me provides more relevance when I am searching. Though it is great that I can find keywords, relevancy and efficiency are what makes me most effective

 

Superhuman Powers are Not Taking A Sourcers Job

Though it may seem that machines are taking your job as a Sourcer away, this is completely untrue.  Instead, machines are assisting in making you more effective and efficient. Sourcers are the analysts that feed the information to the computer. Someone needs to understand how the candidate is relevant to the action they are performing so that they can analyze the results for accuracy.

This article isn’t meant to discourage you from boolean by any means but rather to examine how semantic can another bit of ammo to add your sourcing toolbox. How many purple squirrels do you think you may have missed while only using Boolean?

Related:  Use Less Boolean In Google For Better Productivity

 

 

Natalya Kazim is a Sourcing Consultant with 15+ years of experience in the Recruitment world. She has had the opportunity to work in several Fortune 500 companies to help lead the initiative to develop their sourcing function.  Natalya is passionate about learning new, innovative, and efficient ways deliver the best quality results.  She has served as both a Mentor and Trainer sharing her wealth of information to help others succeed. She has a strong background in advanced sourcing, competitive intel research, organizational charting, market analysis, candidate information retrieval, and passive candidate engagement . Natalya resides in the Washington, DC Metro Area.

 

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