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Approximate Query on Temporal Knowledge Graphs via Two-Level Embeddings [PDF]
EntropyApproximate query on knowledge graphs (KGs) is an important and common task in real-world applications, where the goal is to return more results on KGs that match the query criteria. Previous approximate query methods have focused on static KGs. However,
Jiaxuan Liu, Xinyi Duan, Luyi Bai
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Question Answering Over Temporal Knowledge Graphs [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area.
Apoorv Saxena +2 more
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Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs [PDF]
Frontiers in Research Metrics and Analytics, 2023 The COVID-19 pandemic highlighted two critical barriers hindering rapid response to novel pathogens. These include inefficient use of existing biological knowledge about treatments, compounds, gene interactions, proteins, etc.
Alessandro Negro +6 more
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Revolutionary Strategy for Depicting Knowledge Graphs with Temporal Attributes [PDF]
MathematicsIn practical applications, the temporal completeness of knowledge graphs is of great importance. However, previous studies have mostly focused on static knowledge graphs, generally neglecting the dynamic evolutionary properties of facts.
Sihan Li, Qi Li
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Unsupervised Entity Alignment for Temporal Knowledge Graphs
CoRR, 2023 Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention.
Xiaoze Liu +4 more
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Summarizing Entity Temporal Evolution in Knowledge Graphs [PDF]
Companion Proceedings of The 2019 World Wide Web Conference, 2019 Companion Proceedings of The 2019 World Wide Web Conference on - WWW '19 The 2019 World Wide Web Conference, WWW '19, San Francisco, USA, 13 May 2019 - 17 May 2019; New York, NY : ACM Press 961-965 (2019).
Mayesha Tasnim +4 more
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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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AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query model
International Journal of Digital Earth, 2023 As an emerging knowledge representation model in the domain of knowledge graphs, geographic knowledge graph can take full advantage of semantic, spatial and temporal information to facilitate answering spatio-temporal questions and completing relations ...
Bing Han +6 more
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Event-Centric Temporal Knowledge Graph Construction: A Survey
Mathematics, 2023 Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information.
Timotej Knez, Slavko Žitnik
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Temporal knowledge graph reasoning triggered by memories
Applied Intelligence, 2023 Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually attracts the attention of researchers since no direct history facts for prediction.
Mengnan Zhao 0001 +3 more
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