Results 31 to 40 of about 244,866 (345)
QubitE:Qubit Embedding for Knowledge Graph Completion [PDF]
Jisuanji kexue, 2023 The knowledge graph completion task completes the knowledge graph by predicting missing facts in the knowledge graph.The quantum-based knowledge graph embedding(KGE) model uses variational quantum circuits to score triples by mea-suring the probability ...
LIN Xueyuan, E Haihong , SONG Wenyu, LUO Haoran, SONG Meina
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IEEE Access, 2023 With the further development of knowledge graphs, many weighted knowledge graphs (WKGs) have been published and greatly promote various applications. However, current deterministic knowledge graph embedding algorithms cannot encode weighted knowledge ...
Kong Wei Kun +6 more
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Embedding Knowledge Graph in Function Spaces
Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementWe introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques ...
Teyou, Louis Mozart Kamdem +2 more
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Comprehensive Survey of Loss Functions in Knowledge Graph Embedding Models [PDF]
Jisuanji kexue, 2023 Due to its rich and intuitive expressivity,knowledge graph has received much attention of many scholars. A lot of works have been accumulated in knowledge graph embedding.
SHEN Qiuhui, ZHANG Hongjun, XU Youwei, WANG Hang, CHENG Kai
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Knowledge Graph Embedding: An Overview [PDF]
APSIPA Transactions on Signal and Information Processing, 2023 Many mathematical models have been leveraged to design embeddings for representing Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks.
Xiou Ge +3 more
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Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models [PDF]
The Web Conference, 2023 Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric ...
Cosimo Gregucci +3 more
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Language Model Guided Knowledge Graph Embeddings
IEEE Access, 2022 Knowledge graph embedding models have become a popular approach for knowledge graph completion through predicting the plausibility of (potential) triples.
Mirza Mohtashim Alam +6 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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Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding [PDF]
IEEE International Joint Conference on Neural Network, 2023 Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, existing NS methods are unsuitable when multi-modal information is considered
Yichi Zhang, Mingyang Chen, Wen Zhang
semanticscholar +1 more source
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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