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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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Knowledge Graph Embedding: An Overview
APSIPA Transactions on Signal and Information ProcessingMany mathematical models have been leveraged to design em-beddings for representing Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks. These mathematically-inspired models are not only highly scalable for inference in large KGs, but also have many explainable advantages in modeling different relation patterns ...
Xiou Ge +3 more
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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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Knowledge graph embeddings: open challenges and opportunities [PDF]
, 2023 While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph ...
Russa Biswas +9 more
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Debiasing knowledge graph embeddings [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. As graph embeddings begin to be used more widely in NLP pipelines, there is a need to develop training methods which remove such biases.
Joseph Fisher +3 more
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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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Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning [PDF]
The Web Conference, 2023 Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of ...
Xiangrong Zhu, Guang-pu Li, Wei Hu
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InGram: Inductive Knowledge Graph Embedding via Relation Graphs [PDF]
International Conference on Machine Learning, 2023 Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training.
Jaejun Lee +2 more
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Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces [PDF]
ACM Computing Surveys, 2022 Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning ...
Jiahang Cao +3 more
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Structured query construction via knowledge graph embedding
, 2022 S.1819-1846In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions.
Decker, S. +4 more
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