Results 31 to 40 of about 8,119,426 (288)

Knowledge Graph Completion with Counterfactual Augmentation

Proceedings of the ACM Web Conference 2023, 2023
Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of them are designed to learn from the observed graph structure, which appears to have imbalanced relation distribution during the training stage.
Heng Chang, Jie Cai, Jia Li 0009
openaire   +3 more sources

Temporal Knowledge Graph Completion Using Box Embeddings

, 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 ...
Abboud, Ralph, Messner, Johannes, Ceylan, Ismail Ilkan, Ralph Abboud, Johannes Messner, Ismail Ilkan Ceylan   +5 more
core   +1 more source

Knowledge Enhanced Graph Neural Networks for Graph Completion [PDF]

, 2023
International audienceGraph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and incomplete. As a result, graph
Chlyah, Sarah, Genevès, Pierre, Werner, Luisa, Layaïda, Nabil   +3 more
core   +3 more sources

RAGAT: Relation Aware Graph Attention Network for Knowledge Graph Completion

IEEE Access, 2021
Knowledge graph completion (KGC) is the task of predicting missing links based on known triples for knowledge graphs. Several recent works suggest that Graph Neural Networks (GNN) that exploit graph structures achieve promising performance on KGC.
Xiyang Liu, Huobin Tan, Qinghong Chen, Guangyan Lin   +3 more
doaj   +1 more source

JOINT FEATURES-BASED KNOWLEDGE GRAPH COMPLETION [PDF]

International Journal of Intelligent Computing and Information Sciences
Knowledge graphs (KGs) help in resolving data inconsistencies and redundancies by organizing information in a unified structure, paving the way for building scalable, interpretable AI systems, as they provide a transparent way to trace reasoning paths ...
Maha Farghaly, Mostafa Aref, Mahmoud Mounir   +2 more
doaj   +1 more source

Rethinking Graph Convolutional Networks in Knowledge Graph Completion

Proceedings of the ACM Web Conference 2022, 2022
Graph convolutional networks (GCNs) -- which are effective in modeling graph structures -- have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC models first use GCNs to generate expressive entity representations and then use knowledge graph embedding (KGE) models to capture the interactions among entities and relations ...
Zhanqiu Zhang, Jie Wang 0005, Jieping Ye, Feng Wu 0001   +3 more
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Research About Knowledge Graph Completion Based on Active Learning

Jisuanji kexue yu tansuo, 2020
Knowledge graph completion focuses on how to improve the missing information in knowledge graph. Knowledge graph completion task has many applications, for example, it can be applied to the knowledge graph of rail transit system to support the system ...
CHEN Qinkuang, CHEN Ke, WU Sai, SHOU Lidan, CHEN Gang
doaj   +1 more source

Fast Knowledge Graph Completion using Graphics Processing Units [PDF]

, 2023
Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations.
Kang, Dong-oh, Song, Hwa Jeon, Lee, Chun-Hee   +2 more
core   +1 more source

Few-Shot Knowledge Graph Completion

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for every relation. The real case is that for most of the relations, very few entity pairs are available.
Chuxu Zhang, Huaxiu Yao, Chao Huang 0001, Meng Jiang 0001, Zhenhui Li, Nitesh V. Chawla   +5 more
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Overview of Research on Knowledge Graph Completion [PDF]

Jisuanji kexue yu tansuo
Knowledge graphs have been widely applied in various fields and have significantly advanced the development of artificial intelligence tasks. However, knowledge graphs still face the challenge of incompleteness, which severely limits their effectiveness ...
Anggeluma, WANG Siriguleng, SI Qintu
doaj   +1 more source