What are semantics and examples?
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?
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.