Results 31 to 40 of about 353,222 (280)

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

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 ...
Zhang, Zhanqiu, Wang, Jie, Ye, Jieping, Wu, Feng   +3 more
openaire   +2 more sources

Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer [PDF]

Findings of the Association for Computational Linguistics: EMNLP 2020, 2020
Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings. While existing KG embedding approaches mainly learn and predict facts within a single KG, a more plausible solution would benefit from the knowledge in ...
Chen, Xuelu, Chen, Muhao, Fan, Changjun, Uppunda, Ankith, Sun, Yizhou, Zaniolo, Carlo   +5 more
openaire   +2 more sources

Graph Attention Networks With Local Structure Awareness for Knowledge Graph Completion

IEEE Access, 2020
Graph neural networks have been proven to be very effective for representation learning of knowledge graphs. Recent methods such as SACN and CompGCN, have achieved the most advanced results in knowledge graph completion.
Kexi Ji, Bei Hui, Guangchun Luo
doaj   +1 more source

Medical Knowledge Graph Completion Based on Word Embeddings

Information, 2022
The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data.
Mingxia Gao, Jianguo Lu, Furong Chen
doaj   +1 more source

Fast knowledge graph completion using graphics processing units

Journal of Parallel and Distributed Computing, 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. It is called knowledge graph completion.
Chun-Hee Lee, Dong-oh Kang, Hwa Jeon Song   +2 more
openaire   +2 more sources

Knowledge Graph Entity Type Completion Based on Neighborhood Aggregation and CNN [PDF]

Jisuanji gongcheng, 2023
Existing entity type completion models of the knowledge graph solve the entity types missing in the knowledge graph by modeling entities and entity types.However, they do not effectively use the relationships between entities, which results in the poor ...
ZOU Changlong, AN Jingmin, LI Guanyu
doaj   +1 more source

Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion

Transactions of the Association for Computational Linguistics, 2021
To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs and many research efforts have been devoted to the automatic construction of CKGs.
Jiayuan Huang, Yangkai Du, Shuting Tao, Kun Xu, Pengtao Xie   +4 more
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Knowledge Graph Completion to Predict Polypharmacy Side Effects

, 2018
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate
AM Manicone, D Sridhar, D Szklarczyk, DA Fishman, F Cheng, GE Hinton, HG Munshi, M Kuhn, M Zitnik, N. P. Tatonetti, W Zhang   +10 more
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Knowledge Graph Completion with Knowledge Enhancement and Contrastive Learning [PDF]

Jisuanji gongcheng
Knowledge Graph Completion(KGC) is an important means of improving the quality of KGs. Existing methods for KGC are mainly divided into structure- and description-based methods.
Juan LIU, Youxiang DUAN, Yuxi LU, Lu ZHANG
doaj   +1 more source