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Semantic-Enhanced Knowledge Graph Completion

Mathematics
Knowledge graphs (KGs) serve as structured representations of knowledge, comprising entities and relations. KGs are inherently incomplete, sparse, and have a strong need for completion.
Xu Yuan, Jiaxi Chen, Yingbo Wang, Anni Chen, Yiou Huang, Wenhong Zhao, Shuo Yu   +6 more
doaj   +3 more sources

Open-World Knowledge Graph Completion

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace.
Shi, Baoxu, Weninger, Tim
core   +3 more sources

Knowledge Graph Completion via Complex Tensor Factorization [PDF]

, 2017
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs—labeled directed graphs—and predicting missing relationships—labeled edges.
Bouchard, G, Dance, CR, Gaussier, E, Riedel, S, Trouillon, T, Welbl, J   +5 more
core   +5 more sources

Drug repurposing for COVID-19 via knowledge graph completion. [PDF]

J Biomed Inform, 2021
Zhang R, Hristovski D, Schutte D, Kastrin A, Fiszman M, Kilicoglu H.   +5 more
europepmc   +2 more sources

Knowledge Graph Completeness: A Systematic Literature Review [PDF]

IEEE Access, 2021
The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to indicate its fitness for use in an application. Several quality dimensions are identified, such as accuracy, completeness, timeliness, provenance, and accessibility, which are used to assess the quality. While many prior studies offer a landscape view of data quality
Issa, Subhi, Adekunle, Onaopepo, Hamdi, Fayçal, Sisaid-Cherfi, Samira, Dumontier, Michel, Zaveri, Amrapali   +5 more
openaire   +4 more sources

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 ...
Shao, Pengpeng, Yang, Guohua, Zhang, Dawei, Tao, Jianhua, Che, Feihu, Liu, Tong   +5 more
openaire   +2 more sources

Generative Transformer with Knowledge-Guided Decoding for Academic Knowledge Graph Completion

Mathematics, 2023
Academic knowledge graphs are essential resources and can be beneficial in widespread real-world applications. Most of the existing academic knowledge graphs are far from completion; thus, knowledge graph completion—the task of extending a knowledge ...
Xiangwen Liu, Shengyu Mao, Xiaohan Wang, Jiajun Bu   +3 more
doaj   +1 more source

Knowledge Graph Completion Based on Half-Edge Principle [PDF]

Jisuanji gongcheng, 2020
Existing knowledge graph completion algorithms are time-consuming and inaccurate.To address these problems,this paper proposes a multi-layer convolution model based on half-edge.The model introduces the half-edge principle,and uses the descriptive ...
CHENG Tao, CHEN Heng, LI Guanyu
doaj   +1 more source

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

Two-View Graph Neural Networks for Knowledge Graph Completion

, 2023
To appear in Proceedings of ESWC 2023; 17 pages; 4 tables; 4 ...
Tong, Vinh, Nguyen, Dai Quoc, Phung, Dinh, Nguyen, Dat Quoc   +3 more
openaire   +2 more sources