Results 51 to 60 of about 30,602 (303)
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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Enabling inductive knowledge graph completion via structure-aware attention network [PDF]
, 2023 : Knowledge graph completion (KGC) aims at complementing missing entities and relations in a knowledge graph (KG). Popular KGC approaches based on KG embedding are typically limited to the transductive setting, i.e., all entities must be seen during ...
Jin, Qun +9 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.
core +1 more source
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
doaj +1 more source
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
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
doaj +1 more source
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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Learning Relational Fractals for Deep Knowledge Graph Embedding in Online Social Networks [PDF]
, 2019 Knowledge Graphs (KGs) have deep and impactful applications in a wide-array of information networks such as natural language processing, recommendation systems, predictive analysis, recognition, classification, etc.
Xin Wang +11 more
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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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Locally Adaptive Translation for Knowledge Graph Embedding [PDF]
, 2016 Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss
Wang, Yuanzhuo +4 more
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