Recent technological developments have contributed to the explosive growth of raw data.Artificial intelligence, along with text mining and natural language processing algorithms, can be applied for the development of programs (i.e. Applicant Tracking Systems) capable of screening objectively thousands of resumes in few minutes without bias to identify the best fit for a job opening based on thresholds, specific criteria or scores.These programs usually look for specific keywords; they sort resumes and rank them to determine the job applications that should be further reviewed by recruiters.Each organization or company has its own set of resume screening system,it is important for candidates to know how they work with the aim of improving their keywords selection based on the job opening they are applying to. The problem with huge amount of data could not be processed by structured query language based queries used in relational database management system be used for analysis of big data. So text mining or text analytics is used. Our aim is to be able identify the skills and requirements needed for specific job posting. Getting the job information data for our project was a bit diffficult hence we began by doing some basic web scraping in order to extract job data. The aim of our project is to extract the information from the job posting dataset and use that in a knowldege graph.
A knowledge graph is a database that uses a graph-structured data model to integrate data which includes not only data but also the relationships between the data. The power of knowledge graphs becomes clear once we start thinking about connections. When we want to use a model which contains many inter-relations between properties, knowledge graphs allow us to model the relationship with a very clear structure.With such a graph structure, we have multiple new ways to improve job recommendations
Page Rank Analysis & Knowledge Graph: Personalized Page Rank has been proven to be a very effective ranking tool in the context of personalized recommendations. Page Rank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is referred to as the Page Rank of E and denoted by PR(E). we use this algorithm in order to showcase the rankings of our graph while representing the top 10 job posting data, as their job title , skills and title id. In this case the Title ID is used as the comparing factor for our outputs