Results 21 to 30 of about 34,792 (262)

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
openaire   +2 more sources

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, Xinkai Ge, Shengli Yang, Linmei Hu, Jie Li, Jinwen Zhang   +5 more
doaj   +1 more source

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
doaj   +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
openaire   +3 more sources

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, Zhang Zhongwei, Feng Yan, Yang Bailin   +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

Contextual language models for knowledge graph completion [PDF]

, 2021
Knowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in
Biswas, Russa, Sofronova, Radina, Alam, Mehwish, Sack, Harald   +3 more
openaire   +3 more sources

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
doaj   +1 more source

Collective Knowledge Graph Completion with Mutual Knowledge Distillation

CoRR, 2023
Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years. However, the predictive power of KGC methods is often limited by the completeness of the existing knowledge graphs from different sources and languages.
Weihang Zhang, Ovidiu Serban, Jiahao Sun, Yike Guo   +3 more
openaire   +2 more sources

Multi-level Fusion Knowledge Graph Completion Model [PDF]

Jisuanji kexue yu tansuo
Knowledge graph completion aims to expand and enhance knowledge graphs by predicting missing triples. Multi-modal knowledge graph completion integrates entity ontology information such as entity descriptions, entity images, and entity attributes to ...
YE Zhihong, WU Yunbing, DAI Sichong, ZENG Zhihong
doaj   +1 more source