Results 51 to 60 of about 177,158 (279)

Fast Variational Knowledge Graph Embedding

2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
Embedding of a knowledge graph(KG) entities and relations in the form of vectors is an important aspect for the manipulation of the KG database for several downstream tasks, such as link prediction, knowledge graph completion, and recommendation.
Giri, Pulak Ranjan, Kurokawa, Mori, Saito, Kazuhiro   +2 more
openaire   +2 more sources

Geometry Interaction Knowledge Graph Embeddings

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a single geometric space such as Euclidean space (zero curved), hyperbolic space (negatively curved) or hyperspherical space (positively curved) to maintain their specific ...
Cao, Zongsheng, Xu, Qianqian, Yang, Zhiyong, Cao, Xiaochun, Huang, Qingming   +4 more
openaire   +2 more sources

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.
Demir, Caglar, Ngomo, Axel-Cyrille Ngonga   +1 more
openaire   +2 more sources

Distance Based Korean WordNet(alias. KorLex) Embedding Model

Applied Artificial Intelligence
The objective of this study was to create graph embedding vectors using Korean WordNet (KorLex) and apply them to neural network word-embedding models.
SeongReol Park, JoongMin Shin, Sanghyun Cho, Hyuk-Chul Kwon, Jung-Hun Lee   +4 more
doaj   +1 more source

One-Shot Relational Learning for Knowledge Graphs

, 2018
Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation ...
Chang, Shiyu, Guo, Xiaoxiao, Wang, William Yang, Xiong, Wenhan, Yu, Mo   +4 more
core   +1 more source

Identification of serum protein biomarkers for pre‐cancerous lesions associated with pancreatic ductal adenocarcinoma

Molecular Oncology, EarlyView.
This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns, Jaclyn S. Long, Sergio Lilla, Kelly Hodge, Marcus J. G. W. Ladds, Colin Nixon, Paula Fernández‐Palanca, Sara Zanivan, Pilar Acedo, Stephen P. Pereira, Kevin M. Ryan   +10 more
wiley   +1 more source

Improving FMEA Comprehensibility via Common-Sense Knowledge Graph Completion Techniques

IEEE Access, 2023
The Failure Mode Effect Analysis process (FMEA) is widely used in industry for risk assessment, as it effectively captures and documents domain-specific knowledge. This process is mainly concerned with causal domain knowledge.
Houssam Razouk, Xing Lan Liu, Roman Kern
doaj   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Predicting biomedical relationships using the knowledge and graph embedding cascade model.

PLoS ONE, 2019
Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within ...
Xiaomin Liang, Daifeng Li, Min Song, Andrew Madden, Ying Ding, Yi Bu   +5 more
doaj   +1 more source

Knowledge Graph Embedding with Iterative Guidance from Soft Rules

, 2017
Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules,
Guo, Li, Guo, Shu, Wang, Bin, Wang, Lihong, Wang, Quan   +4 more
core   +1 more source