At Jobsket ATS we have spend years of development and research creating an innovative search engine based in semantic technology and that guarantees the best matching results for your search operations. Semantic search is an evolution in the jobs world that benefits both candidates and hiring deparments, speeding up recruitment processes by returning more relevant search results for your queries.

Probably, you are already tired of finding that most of the job search engines and traditional tools return irrelevant results when you try to search for talent.

Jobsket ATS uses its data extraction and conceptual analysis technology to guarantee better results. Conceptual and contextual analysis ensure that results really match the original queries increasing your productivity and saving time and money in your daily operations.

Semantic Search Basics

Beware that some vendors advertise Semantic Search as what is commonly known as keyword-based searching. Keywords-based searching is basically a plain full text search. On the other hand, semantic search is based on the analysis of all the different data semantics, the location of the data and its contextualization with other data nearby. This analysis is considerably more complex than traditional search but it also guarantees better results.

Conceptual Schemas

At Jobsket ATS we've build our own human resources ontology. We could have done this for any other sectors in which this tools could also be applied like healthcare, Law, finance, etc. But we decided to apply it to human resources as it really makes sense to automate most of the manual procedures that are performed right now.

This conceptualization of common terms allow us to know much better what you are really searching when you enter a query and allows us to give you more exact results. For example, if you are searching for a Developer (a term commonly applied to programmers) you probably will not be interested in search results of Business Developers our Tourism Developers.

Data Relationships

Data relationships are the next important aspect within any semantic search engine. Every concept or entity has got a number of attributes that allow its classification and that help to contextualize and to weight search operation. For example, a networks engineer may have Cisco certifications like CCNA or CCNP. These certifications should weight positively in search results as it means a better profile.

Relationships develop also a fundamental role within automatic data and search results classification. For example, a person that has worked with Forex, Murex, etc. is clearly focused in the Finance world, while on the other hand someone with Java, Oracle or .NET as skills is clearly related with Information Technology.

Contextualization

Contextualization and experience analysis is another fundamental step when trying to weight and return meaningful search results. Recent experience should weight much more at search results. For example, if a candidate worked as a Waiter years ago to pay his or her Engineering studies and this person has never worked back as a Waiter, then definitely this candidate should not be returned as a Waiter in search results.

Internationalization

One last aspect relevant to semantic search is the internationalization. Commonly you will receive CVs in different languages. For example, you may receive CVs in Spanish or Italian if you are looking for people with a fluid level in those languages. As a consequence, you may up with a database with CVs in many different languages. And therefore, search engines should be able to search in different languages.

For example, at Jobsket ATS if you search for a Customer Support Agent with Spanish, you will probably be interested in search results written in Spanish, i.e. CVs in which the textual term is Agente de Soporte al Cliente.