Zobrazeno 1 - 10
of 46 215
pro vyhledávání: '"Zhang, Wen"'
Autor:
Pan, Jeff Z., Razniewski, Simon, Kalo, Jan-Christoph, Singhania, Sneha, Chen, Jiaoyan, Dietze, Stefan, Jabeen, Hajira, Omeliyanenko, Janna, Zhang, Wen, Lissandrini, Matteo, Biswas, Russa, de Melo, Gerard, Bonifati, Angela, Vakaj, Edlira, Dragoni, Mauro, Graux, Damien
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 2:1-2:38 (2023)
Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and par
Externí odkaz:
https://doaj.org/article/49afd43227ff4798bd12c458ce0158d0
Autor:
Zhang, Kexin, Huang, Feng, Liu, Luotao, Xiong, Zhankun, Zhang, Hongyu, Quan, Yuan, Zhang, Wen
The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) assoc
Externí odkaz:
http://arxiv.org/abs/2406.19156
Autor:
Zhang, Wen, Jin, Long, Zhu, Yushan, Chen, Jiaoyan, Huang, Zhiwei, Wang, Junjie, Hua, Yin, Liang, Lei, Chen, Huajun
Natural language question answering (QA) over structured data sources such as tables and knowledge graphs (KGs) have been widely investigated, for example with Large Language Models (LLMs). The main solutions include question to formal query parsing
Externí odkaz:
http://arxiv.org/abs/2406.18916
Autor:
Zhang, Wen, Xu, Yajing, Ye, Peng, Huang, Zhiwei, Xu, Zezhong, Chen, Jiaoyan, Pan, Jeff Z., Chen, Huajun
Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They are triple-level tasks with some elements in a missing triple given to p
Externí odkaz:
http://arxiv.org/abs/2406.18166
Autor:
Wang, Junjie, Chen, Mingyang, Hu, Binbin, Yang, Dan, Liu, Ziqi, Shen, Yue, Wei, Peng, Zhang, Zhiqiang, Gu, Jinjie, Zhou, Jun, Pan, Jeff Z., Zhang, Wen, Chen, Huajun
Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enhance LLMs' performance by combining step-wise planning with external re
Externí odkaz:
http://arxiv.org/abs/2406.14282
Entity matching (EM), the task of identifying whether two descriptions refer to the same entity, is essential in data management. Traditional methods have evolved from rule-based to AI-driven approaches, yet current techniques using large language mo
Externí odkaz:
http://arxiv.org/abs/2406.11255
We propose a theoretical scheme to enhance the sensitivity of a quantum fiber-optical gyroscope (QFOG) via a non-Gaussian-state probe based on quadrature measurements of the optical field. The non-Gaussian-state probe utilizes the product state compr
Externí odkaz:
http://arxiv.org/abs/2406.02217
This study investigates the excesses observed in the diphoton and $b\bar b$ data around $95\;{\rm GeV}$ within the framework of the $B-L$ supersymmetric model (B-LSSM). Comparing with the minimal supersymmetric standard model, the B-LSSM incorporates
Externí odkaz:
http://arxiv.org/abs/2406.01926
Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information
Externí odkaz:
http://arxiv.org/abs/2405.20668
Accurate prediction of molecular properties is critical in the field of drug discovery. However, existing methods do not fully consider the fact that molecules in the real world usually possess multiple property labels, and complex high-order relatio
Externí odkaz:
http://arxiv.org/abs/2405.18724