GraphRank Pro+: Advancing Talent Analytics Through Knowledge Graphs and Sentiment-Enhanced Skill Profiling
This addresses the problem of diverse resume formatting for job recruiters, curriculum designers, and job seekers, though it appears incremental as it builds on existing graph and NLP methods.
The paper tackles the challenge of extracting information from semi-structured resumes by proposing a knowledge graph-based approach, resulting in a system that enables precise extraction and sophisticated querying for talent analysis.
The extraction of information from semi-structured text, such as resumes, has long been a challenge due to the diverse formatting styles and subjective content organization. Conventional solutions rely on specialized logic tailored for specific use cases. However, we propose a revolutionary approach leveraging structured Graphs, Natural Language Processing (NLP), and Deep Learning. By abstracting intricate logic into Graph structures, we transform raw data into a comprehensive Knowledge Graph. This innovative framework enables precise information extraction and sophisticated querying. We systematically construct dictionaries assigning skill weights, paving the way for nuanced talent analysis. Our system not only benefits job recruiters and curriculum designers but also empowers job seekers with targeted query-based filtering and ranking capabilities.