Results 61 to 70 of about 35,565 (288)
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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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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Joint Multilingual Knowledge Graph Completion and Alignment
, 2022 Knowledge graph (KG) alignment and completion are usually treated as two independent tasks. While recent work has leveraged entity and relation alignments from multiple KGs, such as alignments between multilingual KGs with common entities and relations ...
Nguyen, DQ +5 more
core
Knowledge Graph Completion with Knowledge Enhancement and Contrastive Learning [PDF]
Jisuanji gongchengKnowledge 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
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Type-enhanced Inductive Knowledge Graph Completion
, 2023 Inductive knowledge graph completion has gained significant attention due to the dynamic nature of entities and facts in knowledge graphs (KGs). The goal of this task is to predict missing links between entities that are unseen during training.
Ma, S, Wang, Z, Wang, K, Zhuang, Z
core
Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion [PDF]
, 2020 Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in them. In this work, we examine the contribution of geometrical space to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging.
Prodromos Kolyvakis +2 more
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A Few-Shot Knowledge Graph Completion Model With Neighbor Filter and Affine Attention
IEEE AccessIn recent times, extensive scholarly focus has been directed towards the knowledge graph completion (KGC) due to the large number of triples that perform well in training tasks. However, the relations of realistic knowledge graphs (KGs) usually have long-
Hongfang Gong, Yingjing Ding, Minyi Ma
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Active knowledge graph completion [PDF]
Information Sciences, 2022 Pouya Ghiasnezhad Omran +3 more
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Somatic mutational landscape in von Hippel–Lindau familial hemangioblastoma
Molecular Oncology, EarlyView.The causes of central nervous system (CNS) hemangioblastoma in Von Hippel–Lindau (vHL) disease are unclear. We used Whole Exome Sequencing (WES) on familial hemangioblastoma to investigate events that underlie tumor development. Our findings suggest that VHL loss creates a permissive environment for tumor formation, while additional alterations ...
Maja Dembic +5 more
wiley +1 more source