Results 61 to 70 of about 244,866 (345)
BiQUE: Biquaternionic Embeddings of Knowledge Graphs [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics.
Jia Guo, Stanley Kok
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TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation [PDF]
International Conference on Computational Linguistics, 2020 In the last few years, there has been a surge of interest in learning representations of entities and relations in knowledge graph (KG). However, the recent availability of temporal knowledge graphs (TKGs) that contain time information for each fact ...
Chengjin Xu +4 more
semanticscholar +1 more source
Updating Embeddings for Dynamic Knowledge Graphs
CoRR, 2021 Data in Knowledge Graphs often represents part of the current state of the real world. Thus, to stay up-to-date the graph data needs to be updated frequently. To utilize information from Knowledge Graphs, many state-of-the-art machine learning approaches use embedding techniques.
Christopher Wewer +2 more
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Knowledge graph embedding for experimental uncertainty estimation [PDF]
, 2023 Purpose: Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance.
Pernici B., Ramalli E.
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SUKE: Embedding Model for Prediction in Uncertain Knowledge Graph
IEEE Access, 2021 Graph embedding models are widely used in knowledge graph completion (KGC) task. However, most models are based on the assumption that knowledge is completely certain, and this is inconsistent with real-world situations.
Jingbin Wang +3 more
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Knowledge graph embedding for drug repurposing [PDF]
, 2020 LAUREA MAGISTRALEOggigiorno è fondamentale poter saper rispondere in breve tempo a una nuova malattia che si diffonde. Per questo motivo, un approccio convenzionale non è sufficientemente reattivo.
RAMALLI, EDOARDO
core
ChronoR: Rotation Based Temporal Knowledge Graph Embedding [PDF]
AAAI Conference on Artificial Intelligence, 2021 Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs.
A. Sadeghian +3 more
semanticscholar +1 more source
A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
Applied Sciences, 2019 Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge ...
Yanjun Wang +7 more
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Convolutional Complex Knowledge Graph Embeddings [PDF]
, 2021 In this paper, we study the problem of learning continuous vector representations of knowledge graphs for predicting missing links. We present a new approach called ConEx, which infers missing links by leveraging the composition of a 2D convolution with a Hermitian inner product of complex-valued embedding vectors.
Caglar Demir, Axel-Cyrille Ngonga Ngomo
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Embedding Uncertain Knowledge Graphs
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of ...
Xuelu Chen +4 more
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