Building a Knowledge Graph for Job Search using BERT Transformer
A guide on how to create knowledge graph using NER and Relation Extraction
Introduction:
While the NLP field has been growing at an exponential rate for the last two years — thanks to the development of transfer based models — their applications have been limited in scope for the job search field. LinkedIn, the leading company in job search and recruitment, is a good example. While I hold a PhD in Material Science and a Master in Physics, I am receiving job recommendations such as Technical Program Manager at MongoDB and a Go Developer position at Toptal which are both web developing companies that are not relevant to my background. This feeling of irrelevancy is shared by many users and is a cause of big frustration.
Job seekers should have access to the best tools to help them find the perfect match to their profile without wasting time in irrelevant recommendations and manual search...
In general, however, traditional job recommendation systems are based on simple keyword and/or semantic similarity that are usually not well suited to providing good job recommendations since they don’t take into account the interlinks between entities. Furthermore, with the rise of Applicant Tracking Systems (ATS)…
