Results 41 to 50 of about 35,565 (288)
Abnormal entity-aware knowledge graph completion
, 2022 In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service
Sun, Ke +5 more
core +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 +3 more
doaj +1 more source
Exploring Relational Semantics for Inductive Knowledge Graph Completion
, 2022 Knowledge graph completion (KGC) aims to infer missing information in incomplete knowledge graphs (KGs). Most previous works only consider the transductive scenario where entities are existing in KGs, which cannot work effectively for the inductive ...
Song, Zeliang +11 more
core +1 more source
JOINT FEATURES-BASED KNOWLEDGE GRAPH COMPLETION [PDF]
International Journal of Intelligent Computing and Information SciencesKnowledge 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 +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 +3 more
openaire +2 more sources
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
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 +2 more
core +1 more source
A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications
Mathematics, 2023 As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios.
Yong Chen +5 more
doaj +1 more source
Learning Context-based Embeddings for Knowledge Graph Completion
Journal of Data and Information Science, 2022 Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one ...
Pu Fei +3 more
doaj +1 more source
Open-World Knowledge Graph Completion Based on Bayesian Network [PDF]
Jisuanji gongcheng, 2021 The relations among entities in Knowledge Graph(KG) are usually interdependent, and this interdependency can be leveraged to construct more triples based on new entities in open-world data to complete KG.Bayesian Network(BN) is an effective model for ...
LI Xinbai, WU Xinran, YUE Kun
doaj +1 more source