Results 111 to 120 of about 244,866 (345)

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

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 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

CausE: Towards Causal Knowledge Graph Embedding

, 2023
Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC). However, KGE
Zhang, Yichi, Zhang, Wen
core  

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

KGDM: A Diffusion Model to Capture Multiple Relation Semantics for Knowledge Graph Embedding

AAAI Conference on Artificial Intelligence
Knowledge graph embedding (KGE) is an efficient and scalable method for knowledge graph completion. However, most existing KGE methods suffer from the challenge of multiple relation semantics, which often degrades their performance.
Xiao Long, Liansheng Zhuang, Aodi Li, Jiuchang Wei, Houqiang Li, Shafei Wang   +5 more
semanticscholar   +1 more source

NanoMOF‐Based Multilevel Anti‐Counterfeiting by a Combination of Visible and Invisible Photoluminescence and Conductivity

Advanced Functional Materials, Volume 36, Issue 43, 29 May 2026.
This study presents novel anti‐counterfeiting tags with multilevel security features that utilize additional disguise features. They combine luminescent nanosized Ln‐MOFs with conductive polymers to multifunctional mixed‐matrix membranes and powder composites. The materials exhibit visible/NIR emission and matrix‐based conductivity even as black bodies.
Moritz Maxeiner, Ruben Maile, Murat Cuvalli, Andreas Wolf, Ayisha Komal, Robert Oestreich, Christoph Janiak, Karl Mandel, Alexander Knebel, Klaus Müller‐Buschbaum   +9 more
wiley   +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   +2 more sources

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

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   +2 more sources