Tuck-KGC: based on tensor decomposition for diabetes knowledge graph completion model integrating Chinese and Western medicine [PDF]
PeerJ Computer ScienceThe medical knowledge graph is essential for intelligent medical services, encompassing personalized diagnostics, precision therapies, and intelligent consultations, among others.
Jiangtao ZhangSun +4 more
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Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [PDF]
, 2021 Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them.
Bo Wang +5 more
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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
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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
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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
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AYNEC: All you need for evaluating completion techniques in knowledge graphs [PDF]
, 2019 The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples
Ayala Hernández, Daniel +5 more
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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
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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
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Improved Knowledge Graph Completion Method for Capsule Network [PDF]
Jisuanji gongcheng, 2020 In order to complete the missing relations between entities of knowledge graph,this paper proposes an improved knowledge graph completion method for capsule network.First,the triplets are presented as a 3-column matrix,which is convolved with multiple ...
WANG Weimei, SHI Yimin, LI Guanyu
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A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion
Information, 2022 Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion.
Haoliang Peng, Yue Wu
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