Results 241 to 250 of about 177,158 (279)

LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?

Advanced Intelligent Discovery, EarlyView.
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler, Manuel Raimann, Marc Münker, Maxim Rosin, Joaquin E. Urrutia Gómez, Christof M. Niemeyer, Markus Reischl, Luca Rettenberger   +7 more
wiley   +1 more source

Variance-stabilized cognitive diagnosis via GCN-enhanced graph attention with adaptive relation pruning. [PDF]

Sci Rep
Zhang X, Dai Z, Zhu G, Wang S, Qian Q.
europepmc   +1 more source

Enhancing knowledge graph recommendations through deep reinforcement learning. [PDF]

Sci Rep
Zhou J, Shen D, Guo Y, Zhao Y, Zhang H.
europepmc   +1 more source

Explainable drug side effect prediction in central neural system via biologically informed graph neural network. [PDF]

Transl Psychiatry
Huang T, Lin KH, Machado-Vieira R, Soares JC, Jiang X, Kim Y.   +5 more
europepmc   +1 more source

Addressing model overcomplexity in drug-drug interaction prediction with molecular fingerprints. [PDF]

J Cheminform
Gil-Sorribes M, Molina A.
europepmc   +1 more source

Attribute knowledge and KBGAT for predicting the accuracy of the harmonized system code for classifying import and export commodities. [PDF]

Sci Rep
Qi L, Zhang Q, Lin X, Zhang J, Liao M.
europepmc   +1 more source

One Model, Many Cities: A Transferable Social Relationship Inference Framework for Human Mobility Data. [PDF]

Proc ACM SIGSPATIAL Int Conf Adv Inf
Chu C, Shahabi C, Tung E, Shafique K.
europepmc   +1 more source

Cross-Species Aging Knowledge Integration into Agentic AI Platform Uncovers Conserved Mechanisms

Ahuja G, Sharma A, Singh A, Kedia S, Sharma A, Gautam V, Sharma P, Khandelwal A, Ramakrishna S, Muddashetty R, Freude K, Solanki S, Chauhan S, Kumar S, Satija S, Duari S, Arora S, Gupta A, Shome R, Sengupta D, nair D.   +20 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Knowledge graph embedding with concepts

Knowledge-Based Systems, 2019
Abstract Knowledge graph embedding aims to embed the entities and relationships of a knowledge graph in low-dimensional vector spaces, which can be widely applied to many tasks. Existing models for knowledge graph embedding primarily concentrate on entity–relation–entitytriplets, or interact with the text corpus.
Niannian Guan, Dandan Song, Lejian Liao
openaire   +3 more sources

Path-specific knowledge graph embedding

Knowledge-Based Systems, 2018
Abstract Knowledge graph embedding aims to represent entities, relations and multi-step relation paths of a knowledge graph as vectors in low-dimensional vector spaces, and supports many applications, such as entity prediction, relation prediction, etc.
Yantao Jia, Yuanzhuo Wang, Xiaolong Jin, Xueqi Cheng   +3 more
openaire   +3 more sources