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Cited by 13

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Hybrid-TE: Hybrid Translation-Based Temporal Knowledge Graph Embedding

Wang, Zhihao, Li, Xin·2019·2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)
DOI 10.1109/ictai.2019.00205
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Abstract

Knowledge graph embedding has become a promising method for knowledge graph completion. In this work, we propose Hybrid-TE, a hybrid translation-based temporal knowledge graph embedding, which combines two translation models TransD and HyTE for modeling both temporal and multirelational facts. Benefiting from two underlying models, Hybrid-TE first builds entity and relation embeddings in separate vector space for modelling multi-relational facts, and then explicitly learns time information by translational embedding on timespecific hyperplanes. We observe that a simple combination of two models does not lead to a satisfactory predictive precision. We therefore propose to project a triplet to all time-specific hyperplanes on which it is temporally valid. Besides, we also explore extra negative relation samplings that differ from positive samplings in relations. We conduct extensive experiments with real datasets on link prediction, relation prediction and temporal scope prediction. Experiments show significant improvements over previous time-insensitive or time-aware models.

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Citations

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Research works printed but not linked: 4

  1. esteban, 2016, Predicting the co-evolution of event and knowledge graphs, 2016 19th International Conference on Information Fusion (FUSION), 98
  2. leetaru, 2013, Gdelt: Global data on events, location, and tone, ISA Annual Convention
  3. bordes, 2013, Translating embeddings for modeling multi-relational data, Advances in Neural Information Processing Systems 26, 2787
  4. trivedi, 2017, Know-evolve: Deep temporal reasoning for dynamic knowledge graphs, Proceedings of the 34th International Conference on Machine Learning - Volume 70 ser ICML', 3462

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Metadata

Authors
Wang, Z., & Li, X.
Affiliations
East China Normal University, China
Venue
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)
Publisher
IEEE
Published
2019-11
Pages
1446-1451
DOI
10.1109/ictai.2019.00205
Cited by
13
References
13
Licence

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CoChat. (2026). Citation check: Hybrid-TE: Hybrid Translation-Based Temporal Knowledge Graph Embedding. https://app.cochat.ai/papers/10.1109/ictai.2019.00205
The paper
Wang, Z., & Li, X. (2019). Hybrid-TE: Hybrid Translation-Based Temporal Knowledge Graph Embedding. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). https://doi.org/10.1109/ictai.2019.00205

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