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GraphRank Pro+: Advancing Talent Analytics Through Knowledge Graphs and Sentiment-Enhanced Skill Profiling

arXiv 2502.18315 ↗First posted 25 Feb 2025cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 18 references that are in the map. First posted on arXiv 25 Feb 2025.

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References · 1

  1. 2013NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweetscited by 33

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