{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T05:19:22Z","timestamp":1763097562771,"version":"3.45.0"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["96917","2025M774474"],"award-info":[{"award-number":["96917","2025M774474"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Person\u2013job 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\u2014such as profiling, relational reasoning, and position matching\u2014supporting more reliable, time-aware decision-making in complex organizations.<\/jats:p>","DOI":"10.3390\/bdcc9110287","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T09:59:09Z","timestamp":1763027949000},"page":"287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of a Person\u2013Job Temporal Knowledge Graph Using Large Language Models"],"prefix":"10.3390","volume":"9","author":[{"given":"Zhongshan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Junzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5870-928X","authenticated-orcid":false,"given":"Xiang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Mingyu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/1748-8583.12090","article-title":"HR and Analytics: Why HR Is Set to Fail the Big Data Challenge","volume":"26","author":"Angrave","year":"2016","journal-title":"Hum. 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