Results 41 to 50 of about 7,645,086 (295)
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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Quaternion Knowledge Graph Embeddings
CoRR, 2019 Accepted by NeurIPS ...
Shuai Zhang 0007 +3 more
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Knowledge Graph Embedding Compression [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings consume a large amount of storage and memory. This is problematic and prohibits the deployment of these techniques in many real world settings. Thus, we
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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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Hypernetwork Knowledge Graph Embeddings [PDF]
, 2019 Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated
Ivana Balazevic +2 more
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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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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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Genus Distribution for a Graph [PDF]
, 2009 In this paper we develop the technique of a distribution decomposition for a graph. A formula is given to determine genus distribution of a cubic graph.
Liangxia, Wan +2 more
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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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