Leveraging Knowledge Graphs for Orphan Entity Allocation in Resume Processing
This work addresses talent acquisition challenges by improving resume processing, though it appears incremental as it integrates existing techniques like knowledge graphs and entity recognition.
The research tackled the problem of processing unstructured resume data by using knowledge graphs for orphan entity allocation, resulting in enhanced efficiency and accuracy in candidate-job matching through automated screening.
Significant challenges are posed in talent acquisition and recruitment by processing and analyzing unstructured data, particularly resumes. This research presents a novel approach for orphan entity allocation in resume processing using knowledge graphs. Techniques of association mining, concept extraction, external knowledge linking, named entity recognition, and knowledge graph construction are integrated into our pipeline. By leveraging these techniques, the aim is to automate and enhance the efficiency of the job screening process by successfully bucketing orphan entities within resumes. This allows for more effective matching between candidates and job positions, streamlining the resume screening process, and enhancing the accuracy of candidate-job matching. The approach's exceptional effectiveness and resilience are highlighted through extensive experimentation and evaluation, ensuring that alternative measures can be relied upon for seamless processing and orphan entity allocation in case of any component failure. The capabilities of knowledge graphs in generating valuable insights through intelligent information extraction and representation, specifically in the domain of categorizing orphan entities, are highlighted by the results of our research.