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Uchanganuzi wa Grafu za Maarifa za Muda

Uchanganuzi wa Grafu za Maarifa za Muda unapanua mbinu za kawaida za grafu za maarifa kwa data ambapo ukweli na mahusiano hubeba mihuri ya muda au vipindi vya uhalali. Huwezesha kufikiri kuhusu jinsi huluki na mahusiano zinavyoendelea kwa muda, kuunga mkono majukumu kama vile utabiri wa viungo kwa ukweli wa siku zijazo, uainishaji wa mahusiano ya muda, na utabiri wa matukio katika data ya mahusiano inayobadilika.

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Vyanzo

  1. Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017). Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs. Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 3462–3471. link
  2. Dasgupta, S. S., Ray, S. N., & Talukdar, P. (2018). HyTE: Hyperplane-based temporally aware knowledge graph embedding. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2001–2011. DOI: 10.18653/v1/D18-1225

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Temporal Knowledge Graph Analysis (TKG Analysis). ScholarGate. https://scholargate.app/sw/network-analysis/temporal-knowledge-graph-analysis

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ScholarGateTemporal Knowledge Graph Analysis (Temporal Knowledge Graph Analysis (TKG Analysis)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/network-analysis/temporal-knowledge-graph-analysis · Seti ya data: https://doi.org/10.5281/zenodo.20539026