Results 81 to 90 of about 30,602 (303)
Croppable Knowledge Graph Embedding
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)Knowledge Graph Embedding (KGE) is a common approach for Knowledge Graphs (KGs) in AI tasks. Embedding dimensions depend on application scenarios. Requiring a new dimension means training a new KGE model from scratch, increasing cost and limiting efficiency and flexibility. In this work, we propose a novel KGE training framework MED.
Yushan Zhu +5 more
openaire +3 more sources
Knowledge Graph Embedding by Normalizing Flows
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified perspective of embedding and introduce uncertainty into KGE from the view of group theory.
XIAO, Changyi, HE, Xiangnan, CAO, Yixin
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Molecular Oncology, EarlyView.BCL9 and BCL9L drive bladder cancer progression by enhancing β‐catenin signaling, promoting proliferation, migration, invasion, and organoid growth. Genetic depletion of BCL9(L) suppresses malignant phenotypes, while pharmacological disruption of the β‐catenin/BCL9(L) complex with ZW4864 inhibits canonical Wnt signaling and tumor‐associated cellular ...
Roland Kotolloshi +11 more
wiley +1 more source
Universal Knowledge Graph Embeddings
Companion Proceedings of the ACM Web Conference 20245 pages, 3 ...
N'Dah Jean Kouagou +6 more
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Translating whole‐genome doubling into precision medicine in cancer
Molecular Oncology, EarlyView.Whole‐genome doubling creates a WGD‐positive tumor state characterized by persistent chromosomal instability, karyotypic diversification, and cellular stress. These same biological pressures drive aggressive tumor evolution while exposing therapeutic vulnerabilities, providing a rationale for WGD‐informed precision medicine. Whole‐genome doubling (WGD)
Sejung Lee, Junghyeok Lim, Jinhyuk Bhin
wiley +1 more source
Network Embedding Learning in Knowledge Graph [PDF]
, 2019 University of Technology Sydney. Faculty of Engineering and Information Technology.Knowledge Graph stores a large number of human knowledge facts in form of multi-relational network structure, is widely used as a core technique in real-world applications
Zhou, Zili
core
RoCS: Knowledge Graph Embedding Based on Joint Cosine Similarity [PDF]
, 2023 Knowledge graphs usually have many missing links, and predicting the relationships between entities has become a hot research topic in recent years. Knowledge graph embedding research maps entities and relations to a low-dimensional continuous space ...
Xiuyuan Guo +3 more
core +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Knowledge Graph Embedding for Hyper-Relational Data
Tsinghua Science and Technology, 2017 Knowledge graph representation has been a long standing goal of artificial intelligence. In this paper, we consider a method for knowledge graph embedding of hyper-relational data, which are commonly found in knowledge graphs.
Chunhong Zhang +4 more
doaj +1 more source
Probabilistic Coarsening for Knowledge Graph Embeddings
Axioms, 2023 Knowledge graphs have risen in popularity in recent years, demonstrating their utility in applications across the spectrum of computer science. Finding their embedded representations is thus highly desirable as it makes them easily operated on and ...
Marcin Pietrasik, Marek Z. Reformat
doaj +1 more source