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 arrive in the field of recruitment, and in a few years, nothing will be the same again. Recruiters and HR teams can already use advanced software that offers 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 essential to understand better 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 on 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 recruitment task rather than lengthy administrative tasks.
In recent years, Applicant Tracking Systems (ATS) such as Inrecruiting have experienced an incredible evolution, so much so that today it is possible to distinguish 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 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.
The advantages are evident with an ATS that implements a semantic analysis search tool like Inda. The semantic search includes the meaning of words and the user’s intention. In this way, during a query (searching for a candidate in the CV database), the recruiter no longer needs to enter the exact search words. There is no risk of discarding qualified candidates using different words in their CVs.
For 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 apparent: using semantic search, it’s possible to halve the time spent on CV screening without sifting through hundreds of CVs, job titles, skills, synonyms, and related terms candidates use.
In conclusion, let’s try to draw up a quick list of the possible advantages that semantic search offers to the recruiter: