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Temporal vidennetværksanalyse

Temporal vidennetværksanalyse udvider standardmetoder for vidennetværk til data, hvor fakta og relationer bærer tidsstempler eller gyldighedsintervaller. Den muliggør ræsonnement om, hvordan entiteter og relationer udvikler sig over tid, og understøtter opgaver som linkforudsigelse for fremtidige fakta, klassifikation af temporale relationer og begivenhedsforudsigelse i dynamiske relationelle data.

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Kilder

  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

Sådan citerer du denne side

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

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ScholarGateTemporal Knowledge Graph Analysis (Temporal Knowledge Graph Analysis (TKG Analysis)). Hentet 2026-06-15 fra https://scholargate.app/da/network-analysis/temporal-knowledge-graph-analysis · Datasæt: https://doi.org/10.5281/zenodo.20539026