Year: 2025
Type: article
Abstract: Person–job data are multi-source, heterogeneous, and strongly temporal, making knowledge modeling and analysis challenging. We present an automated approach for constructing a Human-Resources Temporal Knowledge Graph. We first formalize a schema in which temporal relations are represented as sets of time intervals. On top of this schema, a large language model (LLM) pipeline extracts entities, relations, and temporal expressions, augmented by self-verification and external knowledge injection to enforce schema compliance, resolve ambiguities, and automatically repair outputs. Context-aware prompting and confidence-based escalation further improve robustness. Evaluated on a corpus of 2000 Chinese resumes, our method outperforms strong baselines, and ablations confirm the necessity and synergy of each component; notably, temporal extraction attains an F1 of 0.9876. The proposed framework provides a reusable path and engineering foundation for downstream HR tasks—such as profiling, relational reasoning, and position matching—supporting more reliable, time-aware decision-making in complex organizations.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc9110287
Authors: Zhongshan Zhang, Junzhi Wang, Bo Li, Xiang Lin, Mingyu Liu
Institution: National University of Defense Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
Awards: G3850178891, G8170027874
OpenAlex ID: W4416167169