Results 101 to 110 of about 244,866 (345)

KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding

AAAI Conference on Artificial Intelligence
Trajectory similarity computation serves as a fundamental functionality of various spatial information applications. Although existing deep learning similarity computation methods offer better efficiency and accuracy than non-learning solutions, they are
Zhen Chen, Dalin Zhang, Shanshan Feng, Kai Chen, Lisi Chen, Peng Han, Shuo Shang   +6 more
semanticscholar   +1 more source

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

Rule-based data augmentation for knowledge graph embedding

AI Open, 2021
Knowledge graph (KG) embedding models suffer from the incompleteness issue of observed facts. Different from existing solutions that incorporate additional information or employ expressive and complex embedding techniques, we propose to augment KGs by ...
Guangyao Li, Zequn Sun, Lei Qian, Qiang Guo, Wei Hu   +4 more
doaj   +1 more source

Learning Relational Fractals for Deep Knowledge Graph Embedding in Online Social Networks

, 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, Wang, Dianwei, Zhang, Ji, Josh Jia-Ching Ying, Wang, Xin, Dianwei Wang, Tao, Xiaohui, Tan, Leonard, Xiaohui Tao, Ji Zhang, Leonard Tan, Ying, Josh Jia-Ching   +11 more
core   +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

Leveraging literals for knowledge graph embeddings

, 2021
Wissensgraphen (Knowledge Graphs, KGs) repräsentieren strukturierte Fakten, die sich aus Entitäten und den zwischen diesen bestehenden Relationen zusammensetzen. Um die Effizienz von KG-Anwendungen zu maximieren, ist es von Vorteil, KGs in einen niedrigdimensionalen Vektorraum zu transformieren.
openaire   +5 more sources

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

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

Locally Adaptive Translation for Knowledge Graph Embedding

, 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, Lin, Hailun, Jin, Xiaolong, Jia, Yantao, Cheng, Xueqi   +4 more
core   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source