Results 11 to 20 of about 9,217,308 (160)

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, Kaffee, Lucie-Aimée, Cochez, Michael, Dumbrava, Stefania, Jendal, Theis E., Lissandrini, Matteo, Lopez, Vanessa, Mencía, Eneldo Loza, Paulheim, Heiko, Sack, Harald, Vakaj, Edlira Kalemi, de Melo, Gerard   +11 more
doaj   +4 more sources

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, Luyi Bai, Shuo Han, Mingcheng Zhang   +3 more
doaj   +2 more sources

Diachronic Embedding for Temporal Knowledge Graph Completion

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones–a problem known as KG completion.
Rishab Goel, Seyed Mehran Kazemi, Marcus A. Brubaker, Pascal Poupart   +3 more
openaire   +5 more sources

Pioneering Temporal Knowledge Graph Completion with Large Language Models: A Text Generation Approach

The Journal of Engineering
Traditional temporal knowledge graph completion (TKGC) methods often rely on random encoding or pre‐trained small‐scale language models to initialize entity and relation embeddings. Despite the remarkable reasoning and understanding capabilities of large
Lan Zhao, Wenhui Zhang, Xiaoyu Li, Simin Hu, Boyue Wang, Guangyu Huo   +5 more
doaj   +2 more sources

MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion

IEEE Transactions on Cybernetics
Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-dependent properties and the evolving nature of knowledge over time. TKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, which can often be mixed together.
Jiapu Wang, Boyue Wang, Junbin Gao, Shirui Pan, Tengfei Liu, Baocai Yin, Wen Gao   +6 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, Meng Cao 0003, Jackie Chi Kit Cheung, William L. Hamilton   +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, Yunpu Ma, Bailan He, Volker Tresp   +3 more
openaire   +2 more sources

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, Tengteng Qu, Xiaochong Tong, Haipeng Wang, Hao Liu, Yuhao Huo, Chengqi Cheng   +6 more
doaj   +1 more source

Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification [PDF]

, 2022
A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG ...
Ji, Xiaopeng, Wen, Peng, Li, Yan
core   +1 more source

Time in a Box [PDF]

Proceedings of the 11th Knowledge Capture Conference, 2021
Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statement \textit{may} be associated with a temporal scope, has attracted growing attention.
Ling Cai 0002, Krzysztof Janowicz, Bo Yan 0003, Rui Zhu 0008, Gengchen Mai   +4 more
openaire   +3 more sources