Results 311 to 320 of about 468,662 (346)

Exploring Large Language Models for Knowledge Graph Completion

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph completion.
Liang Yao, Jiazhen Peng, Chengsheng Mao, Yuan Luo   +3 more
semanticscholar   +1 more source

Rethinking Regularization Methods for Knowledge Graph Completion

arXiv.org
Knowledge graph completion (KGC) has attracted considerable attention in recent years because it is critical to improving the quality of knowledge graphs. Researchers have continuously explored various models.
Linyu Li, Zhi Jin, Yuanpeng He, Dongming Jin, Haoran Duan, Zhengwei Tao, Xuan Zhang, Jiandong Li   +7 more
semanticscholar   +1 more source

SemSI-GAT: Semantic Similarity-Based Interaction Graph Attention Network for Knowledge Graph Completion

IEEE Transactions on Knowledge and Data Engineering
Graph Neural Networks (GNNs) show great power in Knowledge Graph Completion (KGC) as they can handle non-Euclidean graph structures and do not depend on the specific shape or topology of the graph.
Xingfei Wang, Ke Zhang, Muyuan Niu, Xiaofeng Wang   +3 more
semanticscholar   +1 more source

NativE: Multi-modal Knowledge Graph Completion in the Wild

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling the triple structure and multi-modal information from entities.
Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Wen Zhang, Hua-zeng Chen   +7 more
semanticscholar   +1 more source

Tackling Sparse Facts for Temporal Knowledge Graph Completion

The Web Conference
Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in ...
Yuchao Zhang, Xiangjie Kong, Kailun Ye, Guojiang Shen, Shangfei Zheng   +4 more
semanticscholar   +1 more source

Neo-TKGC: Enhancing Temporal Knowledge Graph Completion with Integrated Node Weights and Future Information

Web Search and Data Mining
Temporal Knowledge Graph Completion (TKGC) involves predicting and filling in missing facts within time series data, a crucial task with wide-ranging applications across various domains.
Zihan Qiu, Xiaolin Zhou, Chunyan An, Qiang Yang, Zhixu Li   +4 more
semanticscholar   +1 more source

Adaptive Modality Interaction Transformer for Multimodal Knowledge Graph Completion

ACM Transactions on Knowledge Discovery from Data
Knowledge graphs (KGs) are frequently confronted with the challenge of incompleteness, a problem that extends to multimodal knowledge graphs (MKGs). The primary goal of multimodal knowledge graph completion (MKGC) is to predict missing entities within ...
Yue Jian, Miao Zhang, Ziyue Qin, Chuyuan Xie, Kui Xiao, Yan Zhang, Zhifei Li   +6 more
semanticscholar   +1 more source

Few-Shot Knowledge Graph Completion With Star and Ring Topology Information Aggregation

IEEE Transactions on Knowledge and Data Engineering
Few-shot knowledge graph completion (FKGC) addresses the long-tail problem of relations by leveraging a few observed support entity pairs to infer unknown facts for tail-located relations.
Jing Zhao, Xinzhu Zhang, Yujia Li, S. Sun   +3 more
semanticscholar   +1 more source

LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations

IEEE Global Engineering Education Conference
While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms.
Hasan Abu-Rasheed, Constance Jumbo, Rashed Al Amin, Christian Weber, Veit Wiese, Roman Obermaisser, M. Fathi   +6 more
semanticscholar   +1 more source

Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion

International Conference on Language Resources and Evaluation
Multi-modal knowledge graph completion (MMKGC) aims to predict the missing triples in the multi-modal knowledge graphs by incorporating structural, visual, and textual information of entities into the discriminant models.
Yichi Zhang, Zhuo Chen, Lei Liang, Hua-zeng Chen, Wen Zhang   +4 more
semanticscholar   +1 more source