Artificial intelligence and big data are revolutionising many sectors. Today, Machine Learning systems recognize and classify images, sounds and words with increasingly precise results. This revolution is also about to make an arrival in the field of recruitment, and. in a few years nothing will be the same again. Recruiter and HR teams already have the possibility of using advanced softwares that offer a deep understanding of human language and its interpretation (Natural Language Processing) to perform operations that speed up the hiring process and offer intelligent suggestions. For example, semantic search.
If you are a recruiter and want to take advantage of this wave of change, it is important to better understand what is meant by Artificial Intelligence and specifically Semantic Search
Today there is a lot of talk about semantic search in a technological context. But is there a correlation between our increasingly digitalised daily life and the study of the meaning of words?
Here are two brief definitions that help us understand semantic search
The truth is that every day we use multiple digital technological tools that facilitate various routine operations.
Two examples that have conditioned and simplified our day to day lifestyle are:
As we mentioned earlier, AI is also having a significant impact within recruiting:
When does semantic research become truly indispensable in the job of a recruiter? Semantic search comes to the aid of recruiters and HR when they have to manage large quantities of applications, it can help by speeding up many routine activities. This allows recruiters and HR teams to focus on the actual recruitment task rather than lengthy adminitrative tasks.
In recent years, Applicant Tracking Systems (ATS) such as In-recruiting have experienced an incredible evolution, so much so that today it is possible to make a clear distinction between traditional ATS and intelligent ATS.
The traditional ATS allows the recruiter to simplify and manage the entire recruitment process: from the posting job phase, to the application, up to the actual selection and onboarding. However, by not implementing artificial intelligence algorithms, these ATS are structurally linked to the level of competence and input of the user.
With an ATS that implements a semantic analysis search tool, such as Inda, the advantages are obvious. The semantic search includes the meaning of words and the user’s intention. In this way, during a query (doing a search for a candidate in the CV database), the recruiter no longer needs to enter the exact search words. This means there is no risk of discarding qualified candidates who use different words within their CV.
Let’s take an example: let’s say you are looking for a Java programmer. Imagine that several candidates have the required skills, but that in their CVs they have used different terms from those you typed in the search you are doing on your ATS (for example, writing “developer” instead of “programmer”). Within a traditional ATS you will only find candidates who have entered that specific term.
An ATS equipped with semantic search will understand the meaning of the word and related phrases, suggesting to the recruiter the candidates who fall within the correct parameters even if they have used different words in their CVs.
The difference is immediately obvious: using semantic search it’s possible to halve the time spent on CV screening, without having to sift through hundreds of CVs, job titles, skills, synonyms, and related terms used by candidates.
In conclusion, let’s try to draw up a quick list of the possible advantages that semantic search offers to the recruiter: