Results 21 to 30 of about 175,691 (261)
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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Holographic Embeddings of Knowledge Graphs
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs.
Maximilian Nickel +2 more
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Benchmarking Whole Knowledge Graph Embedding Techniques
International Journal of Software Engineering and Knowledge Engineering, 2023 Knowledge Graphs (KGs) are gaining popularity and are being widely used in a plethora of applications. They owe their popularity to the fact that KGs are an ideal form to integrate and retrieve data originating from various sources. Using KGs as input for Machine Learning (ML) tasks allows to perform predictions on these popular graph structures ...
Pieter Bonte +4 more
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Biological applications of knowledge graph embedding models [PDF]
Briefings in Bioinformatics, 2020 AbstractComplex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks.
Mohamed, Sameh K +2 more
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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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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 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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Relation path embedding in knowledge graphs [PDF]
Neural Computing and Applications, 2018 Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths.
Xixun Lin +4 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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