Results 141 to 150 of about 8,119,426 (288)

Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network. [PDF]

Proc Conf Assoc Comput Linguist Meet, 2021
Lovelace J, Newman-Griffis D, Vashishth S, Lehman JF, Rosé CP.   +4 more
europepmc   +1 more source

Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph Completion

, 2022
In this paper, we report experiments on Few- and Zero-shot Knowledge Graph completion, where the objective is to add missing relational links between entities into an existing Knowledge Graph with few or no previous examples of the relation in question ...
Girdzijauskas, Sarunas,, Karlgren, Jussi,, Zhang, Chenda,, Cornell, Filip,   +3 more
core  

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source

Path-based Explanation for Knowledge Graph Completion

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Accepted by KDD ...
Heng Chang, Jiangnan Ye 0003, Alejo Lopez-Avila, Jinhua Du, Jia Li 0009   +4 more
openaire   +2 more sources

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion

Knowledge Graph Completion (KGC) aims to predict the missing [relation] part of (head entity)--[relation]->(tail entity) triplet. Most existing KGC methods focus on single features (e.g., relation types) or sub-graph aggregation. However, they do not fully explore the Knowledge Graph (KG) features and neglect the guidance of external semantic ...
openaire   +3 more sources

Dynamic Fluorescence Modulation via Light‐Driven Competitive Protonation

Advanced Optical Materials, EarlyView.
A two‐component molecular system demonstrates reversible, light‐driven fluorescence write–erase cycling and thermal imaging. Illumination of a merocyanine photoacid transfers a proton to a highly efficient fluorescent pyridinium dye, switching its emission color; thermal relaxation reverses the process in the dark.
Serena X. Liu, Moritz E. Kleybolte, Colin van Dyck, Sergey I. Vagin, Preeti Gahtori, Antonio Scarpati, Razieh Firouzihaji, Diogo A. Galico, Ulrich S. Schubert, Julianne Gibbs, Alkiviathes Meldrum   +10 more
wiley   +1 more source

Knowledge Graphs 2023 - 6.3 Knowledge Graph Completion

, 2023
Sack, Harald, Tan, Mary Ann
openaire   +2 more sources

Scalable Task Planning via Large Language Models and Structured World Representations

Advanced Robotics Research, EarlyView.
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina   +4 more
wiley   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source