Results 21 to 30 of about 7,645,086 (295)
Structured query construction via knowledge graph embedding [PDF]
, 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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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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HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network. [PDF]
Brief BioinformDuan P +10 more
europepmc +2 more sources
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.
Bo Xiong 0001 +6 more
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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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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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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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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 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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What Is Learned in Knowledge Graph Embeddings? [PDF]
, 2022 A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with edge types. KGs are an important primitive in modern machine learning and artificial intelligence. Embedding-based models, such as the seminal TransE [Bordes et al., 2013] and the recent PairRE [Chao et al., 2020] are ...
Michael R. Douglas +6 more
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