Results 1 to 10 of about 175,691 (261)
A type-augmented knowledge graph embedding framework for knowledge graph completion [PDF]
Scientific Reports, 2023 Knowledge graphs (KGs) are of great importance to many artificial intelligence applications, but they usually suffer from the incomplete problem.
Peng He +4 more
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Knowledge Graph Embedding by Dynamic Translation [PDF]
IEEE Access, 2017 Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the ...
Liang Chang +5 more
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Advances in Knowledge Graph Embedding Based on Graph Neural Networks [PDF]
Jisuanji kexue yu tansuo, 2023 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.
YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
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Survey on Applications of Knowledge Graph Embedding in Recommendation Tasks [PDF]
Jisuanji kexue yu tansuo, 2022 Recommendation systems are designed to recommend personalized content to improve user experience. At present, the recommendation systems still face some challenges such as poor interpretability, cold start problem and serialized recommendation modeling ...
TIAN Xuan, CHEN Hangxue
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Real-Time Semantic Data Flow Reasoning Based on Improved Multi-Embedding Space [PDF]
Jisuanji gongcheng, 2022 The joint use of semantic data flow processing engine and knowledge graph embedding representation learning can effectively improve the performance of real-time data stream reasoning and query.The existing knowledge representation learning models pay ...
GAO Feng, YAO Guangtao, GU Jinguang
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Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [PDF]
, 2021 Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them.
Bo Wang +5 more
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IEEE Access, 2023 With the further development of knowledge graphs, many weighted knowledge graphs (WKGs) have been published and greatly promote various applications. However, current deterministic knowledge graph embedding algorithms cannot encode weighted knowledge ...
Kong Wei Kun +6 more
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QubitE:Qubit Embedding for Knowledge Graph Completion [PDF]
Jisuanji kexue, 2023 The knowledge graph completion task completes the knowledge graph by predicting missing facts in the knowledge graph.The quantum-based knowledge graph embedding(KGE) model uses variational quantum circuits to score triples by mea-suring the probability ...
LIN Xueyuan, E Haihong , SONG Wenyu, LUO Haoran, SONG Meina
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Comprehensive Survey of Loss Functions in Knowledge Graph Embedding Models [PDF]
Jisuanji kexue, 2023 Due to its rich and intuitive expressivity,knowledge graph has received much attention of many scholars. A lot of works have been accumulated in knowledge graph embedding.
SHEN Qiuhui, ZHANG Hongjun, XU Youwei, WANG Hang, CHENG Kai
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Language Model Guided Knowledge Graph Embeddings
IEEE Access, 2022 Knowledge graph embedding models have become a popular approach for knowledge graph completion through predicting the plausibility of (potential) triples.
Mirza Mohtashim Alam +6 more
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