Results 21 to 30 of about 351,050 (219)

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

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

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

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
doaj   +1 more source

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   +1 more source

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
core   +1 more source

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning [PDF]

, 2019
Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones.
Bernstein, Abraham, Chen, Huajun, Chen, Jiaoyan, Paudel, Bibek, Wang, Liang, Zhang, Wei, Zhang, Wen, Zhu, Hai   +7 more
core   +1 more source

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

On Multi-Relational Link Prediction with Bilinear Models [PDF]

, 2017
We study bilinear embedding models for the task of multi-relational link prediction and knowledge graph completion. Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good ...
Gemulla, Rainer, Li, Hui, Wang, Yanjie
core   +3 more sources