Results 41 to 50 of about 3,631,772 (250)
OpenAlex Author Name Disambiguation V3 Initial Clusters
Author name disambiguation V3 initial clusters for the OpenAlex dataset. See https://openalex.org There are 633803287 rows, split into 4 CSV (comma-delimited) files (with headers).
Justin Barrett +4 more
core +1 more source
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 +3 more
wiley +1 more source
Evaluating Co-authorship Networks in Author Name Disambiguation for Common Names [PDF]
With the increasing size of digital libraries it has become a challenge to identify author names correctly. The situation becomes more critical when different persons share the same name (homonym problem) or when the names of authors are presented in several different ways (synonym problem).
Fakhri Momeni, Philipp Mayr 0001
openaire +3 more sources
The PRIMA Thesaurus for Materials Science and Engineering
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 +8 more
wiley +1 more source
Volume changes of a solid‐state battery cell are separated into the individual contributions of anode and cathode. Simultaneously determining the “reaction volumes” of both electrodes requires a reference electrode with a pressure‐independent potential.
Mervyn Soans +5 more
wiley +1 more source
Diting: An Author Disambiguation Method Based on Network Representation Learning
It is important to disambiguate names among persons in many scenarios. In this work, we propose an unsupervised method Diting and a semi-supervised method Diting++ for author disambiguation.
Liwen Peng +5 more
doaj +1 more source
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
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 +4 more
wiley +1 more source
Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki +2 more
wiley +1 more source
Also By The Same Author: AKTiveAuthor, a Citation Graph Approach to Name Disambiguation
The desire for definitive data and the semantic web drive for inference over heterogeneous data sources requires co-reference resolution to be performed on those data.
McRae-Spencer, Duncan +3 more
core +1 more source
We proposed a Mixed Reality Sensorized Laryngoscope Training System to provide real‐time holographic torque feedback during pediatric endotracheal intubation simulation. Visualization formats are evaluated to reduce tracking error and visual demand.
Jiaqi Li +5 more
wiley +1 more source

