Results 51 to 60 of about 353,222 (280)

Knowledge Graph Completion With Pattern-Based Methods

IEEE Access
Knowledge graphs (KGs) are popularly used to develop several intelligent applications. Revealing valuable knowledge hidden in these graphs opened up a branch of research, known as KG reasoning, aiming at predicting the missing links.
Maryam Sabet, Mohammadreza Pajoohan, Mohammad Reza Moosavi   +2 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

Knowledge graph completion for scholarly knowledge graph

Bulletin of Social Informatics Theory and Application
Scholarly knowledge graph is a knowledge graph that is used to represent knowledge contained in scientific publication documents. The information we can find in a scientific publication document is as follows: author, institution, name of journal/conference, and research topic. A knowledge graph that has been built is usually still not perfect.
Taufiqurrahman Taufiqurrahman, Kemas Rahmat Saleh Wiharja, Gia Septiana Wulandari   +2 more
openaire   +1 more source

Establishment of a humanized patient‐derived xenograft mouse model of high‐grade serous ovarian cancer for preclinical evaluation of combination immunotherapy

Molecular Oncology, EarlyView.
We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric, Line Bjørge, Martine Rott Lode, Cecilie Fredvik Torkildsen, Pia Aehnlich, Rammah Elnour, Daniela Elena Costea, Lars Andreas Akslen, Liv Cecilie Vestrheim Thomsen, Emmet McCormack, Katrin Kleinmanns   +10 more
wiley   +1 more source

Knowledge graph completion method based on hyperbolic representation learning and contrastive learning

Egyptian Informatics Journal, 2023
Knowledge graph completion employs existing triples to deduce missing data, thereby enriching and enhancing graph completeness. Recent research has revealed that using hyperbolic representation learning in knowledge graph completion yields superior ...
Xiaodong Zhang, Meng Wang, Xiuwen Zhong, Feixu An   +3 more
doaj   +1 more source

CCDC80 suppresses high‐grade serous ovarian cancer migration via negative regulation of B7‐H3

Molecular Oncology, EarlyView.
PAX8 is a lineage‐specific master regulator of transcription in high‐grade serous ovarian cancer (HGSC) progression. We show for the first time that PAX8 facilitates proliferation and metastasis by repressing the cell autonomous tumor suppressor CCDC80 and inducing B7‐H3 expression.
Aya Saleh, Nitzan Medina‐Itzhaki, Milena Chekov, Eden Gal‐Swisa, Roba Gabesh‐Wahabi, Inbar Savyon, Inna Naroditsky, Hilary A. Kenny, Basem Fares, Lina Korsensky, Einav Girsh, Liron Berger, Ruth Perets   +12 more
wiley   +1 more source

Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence

, 2018
Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications.
Lin, Fen, Lin, Leyu, Liu, Zhiyuan, Xie, Ruobing   +3 more
core   +1 more source

Augmenting Embedding Projection With Entity Descriptions for Knowledge Graph Completion

IEEE Access, 2021
Extra information, such as hierarchical entity types, entity descriptions or some text corpus are recently used to enhance Knowledge Graph Completion (KGC).
Junfan Chen, Jie Xu, Manhui Bo, Hongwu Tang   +3 more
doaj   +1 more source

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

, 2018
We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is ...
Cai, Liwei, Wang, William Yang
core   +1 more source

Enhancing Knowledge Graph Completion By Embedding Correlations

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
Despite their large sizes, modern Knowledge Graphs (KGs) are still highly incomplete. Statistical relational learning methods can detect missing links by "embedding" the nodes and relations into latent feature tensors. Unfortunately, these methods are unable to learn good embeddings if the nodes are not well-connected.
Pal, Soumajit, Urbani, Jacopo
openaire   +2 more sources