Results 41 to 50 of about 8,119,426 (288)
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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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 +3 more
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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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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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Abstract Graphs and Abstract Paths for Knowledge Graph Completion [PDF]
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), 2019 Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete. Information that we induce from such graphs – e.g. entity embeddings, relation representations or patterns – will be affected by the imbalance in the information captured in the graph – by biasing representations, or causing us to miss potential patterns.
Vivi Nastase, Bhushan Kotnis
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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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Open-World Knowledge Graph Completion
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing ...
Baoxu Shi, Tim Weninger
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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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RecKGC: Integrating recommendation with knowledge graph completion
, 2019 Both recommender systems and knowledge graphs can provide overall and detailed views on datasets, and each of them has been a hot research domain by itself.
J Wen (9888611) +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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