Results 91 to 100 of about 7,645,086 (295)

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

Knowledge Graph Embedding With Interactive Guidance From Entity Descriptions

IEEE Access, 2019
Knowledge Graph (KG) embedding aims to represent both entities and relations into a continuous low-dimensional vector space. Most previous attempts perform the embedding task using only knowledge triples to indicate relations between entities.
Wen'an Zhou, Shirui Wang, Chao Jiang
doaj   +1 more source

A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings

2025 19th International Conference on Semantic Computing (ICSC)
Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied settings. In particular, KGEMs are typically applied to KGs to solve the link prediction task; i.e. to predict new facts in the domain of a KG based on existing, observed facts.
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan   +2 more
openaire   +4 more sources

Incorporating Literals into Knowledge Graph Embeddings [PDF]

, 2019
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations ...
Agustinus Kristiadi, Mohammad Asif Khan 0001, Denis Lukovnikov, Jens Lehmann 0001, Asja Fischer   +4 more
openaire   +3 more sources

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning

International Journal of Computational Intelligence Systems
As knowledge is time-sensitive, some researchers have started to focus on dynamic knowledge graphs to provide time-dimensioned knowledge content thus reflecting richer information.
Wei Huang, Junling Chen, Dexian Wang, Pengfei Zhang, Jia Liu, Tianrui Li   +5 more
doaj   +1 more source

Embedding Knowledge Graphs in Function Spaces

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques.
Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo   +2 more
openaire   +3 more sources

The PRIMA Thesaurus for Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa, Alexey Boubnov, Dario De Angelis, Catriona Eschke, Maximilian Kabbe, Felix Kraus, Mirco Panighel, Richard Thelen, Davide Valentinis   +8 more
wiley   +1 more source

Knowledge Graph Essentials and Key Technologies

Современные информационные технологии и IT-образование, 2019
In recent decades, the amount of information that humankind has accumulated has increased tremendously. People cannot analyze it effectively using simple algorithms, and data structures due to these approaches do not understand the se¬mantics of the data.
Vladislav Gurin, Eugene Kostrov, Yuliya Gavrilenko, Daniel Saada, Eugene Ilyushin, Ivan Chizhov   +5 more
doaj   +1 more source

Domain Representation for Knowledge Graph Embedding [PDF]

, 2019
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain.
Cunxiang Wang, Feiliang Ren, Zhichao Lin, Chenxu Zhao, Tian Xie, Yue Zhang 0004   +5 more
openaire   +2 more sources