Results 31 to 40 of about 34,792 (262)
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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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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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
Baoxu Shi, Tim Weninger
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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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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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Semantic-Enhanced Knowledge Graph Completion
MathematicsKnowledge 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 +6 more
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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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Pre-training Transformers for Knowledge Graph Completion
CoRR, 2023 Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures. Inspired by Transformer-based pretrained language models' success on learning transferable representation for texts, we introduce a novel inductive KG representation model (iHT) for KG completion by large ...
Sanxing Chen +5 more
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Progressive Knowledge Graph Completion
CoRRKnowledge Graph Completion (KGC) has emerged as a promising solution to address the issue of incompleteness within Knowledge Graphs (KGs). Traditional KGC research primarily centers on triple classification and link prediction. Nevertheless, we contend that these tasks do not align well with real-world scenarios and merely serve as surrogate benchmarks.
Jiayi Li 0002 +4 more
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Two-View Graph Neural Networks for Knowledge Graph Completion
, 2023 To appear in Proceedings of ESWC 2023; 17 pages; 4 tables; 4 ...
Vinh Tong +3 more
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