Results 81 to 90 of about 7,645,086 (295)

Using Knowledge Graph Embedding for Fault Detection

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023
Automotive manufacturers are under stressful timelines as they shift their focus from internal combustion engines (ICE) to electric (EV) and hybrid-electric vehicles (HEV).
Ziad Kobti, Joseph El-Ghaname
doaj   +1 more source

Understanding the Burden of Orofacial Involvement and Patient Treatment Preferences in Systemic Sclerosis: Results From a Large International Survey

Arthritis Care &Research, EarlyView.
Objective Orofacial manifestations are significantly impactful in patients with systemic sclerosis (SSc) yet remain understudied, with no dedicated clinical guidelines to inform their management. Methods An international online survey comprised38 questions addressing orofacial manifestations of SSc, including patients’ confidence in their treating ...
Eleni Deligianni, Lee Shapiro, Deana Gibbs, Zsuzsanna H. McMahan, Michael Hughes   +4 more
wiley   +1 more source

Learning Graph Embedding With Adversarial Training Methods

, 2020
Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction ...
Jiang, Jing, Long, Guodong, Pan, Shirui, Zhang, Chengqi, Fung, Sai-fu, Hu, Ruiqi   +5 more
core   +1 more source

Mapping the Social Network Structure and Composition of Black Individuals With Rheumatic and Musculoskeletal Conditions

Arthritis Care &Research, EarlyView.
Objective Our objective was to describe the social networks of Black individuals with rheumatic and musculoskeletal conditions and understand the clustering of health‐related behaviors to inform future community‐based, peer‐led interventions. Methods We used an adapted Personal Network Survey for Clinical Research (PERSNET) to map the personal social ...
Taussia Boadi, Amar Dhand, Leah Santacroce, Neil Pillai, Komel Safdar, Mia T. Chandler, Ariel Childs, Monica Crespo‐Bosque, Gina Curry, Mary Dollear, Alice Eggleston, Daniel Erickson, Dieufort Fleurissaint, Denice Garrett, Gail Granville, Tyler Green, Magdalena Hamielec, Elena Losina, Karen Mancera‐Cuevas, Mary Ann Nelson, Chisa Nosamiefan, Bisola Ojikutu, Tonya S. Roberson, Mary Beth Son, Marie Jacques Toussaint, Michael York, Rosalind Ramsey‐Goldman, Candace H. Feldman   +27 more
wiley   +1 more source

QLite: Lightweight Knowledge Graph Embedding Framework With Query Processing

IEEE Access
A vast number of studies on knowledge graph embedding have been conducted. However, most knowledge graph embedding models have high dimensional embedding vectors.
Chun-Hee Lee, Dong-Oh Kang
doaj   +1 more source

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 ...
Zongsheng Cao, Qianqian Xu 0001, Zhiyong Yang 0001, Xiaochun Cao, Qingming Huang   +4 more
openaire   +4 more sources

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Interest Capturing Recommendation Based on Knowledge Graph [PDF]

Jisuanji kexue
As a kind of auxiliary information,knowledge graph can provide more context information and semantic association information for the recommendation system,thereby improving the accuracy and interpretability of the recommendation.By mapping items into ...
JIN Yu, CHEN Hongmei, LUO Chuan
doaj   +1 more source

CoKE: Contextualized Knowledge Graph Embedding

CoRR, 2019
Knowledge graph embedding, which projects symbolic entities and relations into continuous vector spaces, is gaining increasing attention. Previous methods allow a single static embedding for each entity or relation, ignoring their intrinsic contextual nature, i.e., entities and relations may appear in different graph contexts, and accordingly, exhibit ...
Quan Wang 0002, Pingping Huang, Haifeng Wang 0001, Songtai Dai, Wenbin Jiang 0002, Jing Liu 0022, Yajuan Lyu, Yong Zhu 0004, Hua Wu 0003   +8 more
openaire   +2 more sources