Results 51 to 60 of about 468,662 (346)
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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Multi-level Fusion Knowledge Graph Completion Model [PDF]
Jisuanji kexue yu tansuoKnowledge 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
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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 +3 more
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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 +5 more
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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
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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
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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
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Reasoning with Forest Logic Programs and f-hybrid Knowledge Bases [PDF]
, 2011 Open Answer Set Programming (OASP) is an undecidable framework for integrating ontologies and rules. Although several decidable fragments of OASP have been identified, few reasoning procedures exist.
Feier, Cristina, Heymans, Stijn
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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 +4 more
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Diachronic Embedding for Temporal Knowledge Graph Completion [PDF]
AAAI Conference on Artificial Intelligence, 2019 Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones–a problem known ...
Rishab Goel +3 more
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