Leveraging Knowledge Graphs for Orphan Entity Allocation in Resume
  Processing
Aagam Bakliwal Shubham Manish Gandhi Yashodhara Haribhakta
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.