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Learning temporal granularity with quadruplet networks for temporal knowledge graph completion [PDF]
Scientific ReportsTemporal Knowledge Graphs (TKGs) capture the dynamic nature of real-world facts by incorporating temporal dimensions that reflect their evolving states. These variations add complexity to the task of knowledge graph completion.
Rushan Geng, Cuicui Luo
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A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
Applied SciencesTemporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-
Ningning Jia, Cuiyou Yao
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Temporal Knowledge Graph Completion Using Box Embeddings
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp.
Johannes Messner +2 more
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Tucker decomposition-based temporal knowledge graph completion [PDF]
Knowledge-Based Systems, 2022 Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years witness that many algorithms for link prediction and knowledge graphs embedding have been designed to ...
Pengpeng Shao +5 more
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Context-aware Temporal Knowledge Graph Completion Based on Relation Constraints [PDF]
Jisuanji kexue, 2023 The existing temporal knowledge graph completion models only consider the structural information of the quadruple itself,ignoring the implicit neighbor information and the constraints of relationships on entities,which leads to the poor perfor-mance of ...
WANG Jingbin, LAI Xiaolian, LIN Xinyu, YANG Xinyi
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Temporal Knowledge Graph Completion: A Survey
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.
Borui Cai +5 more
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Knowledge Graph Embeddings: Open Challenges and Opportunities [PDF]
Transactions on Graph Data and Knowledge, 2023 While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph ...
Biswas, Russa +11 more
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Few-shot temporal knowledge graph completion based on meta-optimization
Complex & Intelligent Systems, 2023 Knowledge Graphs (KGs) have become an increasingly important part of artificial intelligence, and KGs have been widely used in artificial intelligence fields such as intelligent answering questions and personalized recommendation.
Lin Zhu +3 more
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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent ...
Jiapeng Wu +3 more
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A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion
CoRR, 2021 Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest.
Zifeng Ding +3 more
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