Results 11 to 20 of about 175,691 (261)
Efficiently embedding dynamic knowledge graphs
Knowledge-Based Systems, 2022 46 ...
Tianxing Wu +4 more
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Ultrahyperbolic Knowledge Graph Embeddings
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-world KGs, however, are rather heterogeneous, i.e., a KG is composed of multiple distinct hierarchies and non-hierarchical graph structures.
Xiong, Bo +6 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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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
Balazevic, Ivana +2 more
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Knowledge Graph Embeddings [PDF]
, 2012 With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al.
Rosso, Paolo +2 more
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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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An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding
Applied Sciences, 2020 Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks.
Su Jeong Choi +2 more
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Application and evaluation of knowledge graph embeddings in biomedical data [PDF]
PeerJ Computer Science, 2021 Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases.
Mona Alshahrani +2 more
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Review of Research Progress on Knowledge Graph Embedding [PDF]
Jisuanji gongchengWith the continuous development of big data and artificial intelligence technologies, knowledge graph embedding is developing rapidly, and knowledge graph applications are becoming increasingly widespread.
MA Hengzhi, QIAN Yurong, LENG Hongyong, WU Haipeng, TAO Wenbin, ZHANG Yiyang
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Knowledge Graph Embedding Model with Entity Description on Cement Manufacturing Domain [PDF]
Jisuanji kexueTo address the problem that many knowledge graph embedding models lack the consideration of semantic information when performing knowledge embedding and cannot extract the semantic information of entities specialized in cement manufactu-ring domain well ...
ZHOU Honglin, SONG Huazhu, ZHANG Juan
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