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

2025-02-25

Sai

AI Summary

To address the challenges of information extraction from semi-structured resumes, coarse-grained skill semantic modeling, and low accuracy in talent–job matching, this paper proposes an emotion-enhanced, knowledge graph–driven talent modeling framework. Methodologically, it quantifies implicit emotional cues (e.g., “strongly preferred”) in job descriptions and embeds them into skill vectors—enabling, for the first time, emotion-aware skill similarity computation. A dynamic knowledge graph integrating multi-source occupational data is constructed using Neo4j and BERT-based knowledge graph completion (BERT-KGC). Furthermore, a dual-channel emotion-enhanced encoder—comprising RoBERTa and an LSTM-based sentiment gating mechanism—collaborates with a relation-aware graph convolutional network (R-GCN) to jointly model skill semantics and evolutionary relationships. Evaluated on cross-domain LinkedIn and Stack Overflow datasets, the approach achieves a 19.7% improvement in talent–job matching accuracy and attains an F1 score of 0.86 for skill evolution prediction, significantly outperforming state-of-the-art baselines.

Technology Category

Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB CompletionHumans and AI: Emotional IntelligenceMachine Learning: Graph-based Machine Learning

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Problem

• Extracting information from semi-structured resumes
• Transforming raw data into Knowledge Graphs
• Enhancing talent analysis with skill profiling

Innovation

• Knowledge Graphs
• Natural Language Processing
• Deep Learning