Results 21 to 30 of about 8,049,750 (250)
Embedding Knowledge Graph through Triple Base Neural Network and Positive Samples [PDF]
Computer and Knowledge Engineering, 2022 Representation learning on a knowledge graph aims to capture patterns in the knowledge graph as low-dimensional dense distributed representation vectors in the continuous semantic space, which is a powerful technique for predicting missing links in ...
Sogol Haghani, Mohammad Reza Keyvanpour
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Enriching Translation-Based Knowledge Graph Embeddings Through Continual Learning
IEEE Access, 2018 This paper addresses an enrichment of translation-based knowledge graph embeddings. When new knowledge triples become available after a knowledge graph is embedded onto a vector space, the embedding should be enriched with the new triples, but without ...
Hyun-Je Song, Seong-Bae Park
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Knowledge Graph Embedding Technology: A Review
Jisuanji kexue yu tansuo, 2021 Knowledge graph embedding (KGE) is a new research hotspot in the field of knowledge graphs, which aims to apply the translation invariance of word vectors to embedding entities and relationships of the knowledge graph into a low-dimensional vector space ...
SHU Shitai, LI Song+, HAO Xiaohong, ZHANG Liping
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CausE: Towards Causal Knowledge Graph Embedding [PDF]
, 2023 Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC). However, KGE
Zhang, Yichi, Zhang, Wen
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Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet.
PLoS ONE, 2021 Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work.
YueQun Wang +3 more
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Knowledge Graph Embedding via Graph Attenuated Attention Networks
IEEE Access, 2020 Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural
Rui Wang +4 more
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Recommender Systems Based on Graph Embedding Techniques: A Review
IEEE Access, 2022 As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user’s preferred items from millions of candidates by analyzing observed user-item relations.
Yue Deng
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Knowledge Graph Embedding via Metagraph Learning
, 2021 Knowledge graph embedding aims to represent entities and relations in a continuous feature space while preserving the structure of a knowledge graph.
Joyce Jiyoung Whang +3 more
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Triple Context-Based Knowledge Graph Embedding
IEEE Access, 2018 Knowledge graph embedding aims to represent entities and relations of a knowledge graph in continuous vector spaces. It has increasingly drawn attention for its ability to encode semantics in low dimensional vectors as well as its outstanding performance
Huan Gao, Jun Shi, Guilin Qi, Meng Wang
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Knowledge Graph Embedding Based Collaborative Filtering
IEEE Access, 2020 Along with the rapidly increasing massive online data, recommender systems have been used as an effective approach for filtering useful information, which have been widely adopted in many web applications.
Yuhang Zhang, Jun Wang, Jie Luo
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