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arXiv AI

CEON: Circular Economy Ontology Network

arXiv:2606. 02253v1 Announce Type: new Abstract: Increasing the circularity of resource use in our society has been recognized as a path to sustainability, i.

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arXiv AI
Aug 17

From Field Data to Global Food Systems Intelligence: A Semantic Graph Framework for Sustainable Wheat Production

arXiv:2502. 19507v2 Announce Type: replace Abstract: In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a modular, graph-based framework that brings diverse wheat production datasets together into a single, queryable store.

By Nirmal Gelal, Aastha Gautam, Soheil Abadifard, Nico Giordano, Moumita Sen Sarma, Sanaz Saki Norouzi, Claudio Dias da Silva Jr, Jean Ribert Francois, Kathleen M. Jagodnik, Katherine Nelson, Terry Griffin, Xiaomao Lin, Stacy Hutchinson, Stephen M. Welch, Kelsey Andersen Onofre, Romulo Lollato, Pascal Hitzler, Hande K\"u\c{c}\"uk McGinty
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arXiv AI
Aug 24

Ontology-supported AI Model and Dataset Management

The paper introduces an ontology-supported platform designed to facilitate the exchange, usage, and analysis of AI models and datasets. It addresses the need for effective management of AI assets in industrial settings by providing a structured framework that reduces semantic gaps. A real‑time critical systems use case demonstrates the platform’s practical utility.

By Jan Novacek, Ali Ahari, Tobias M\"uller, Sebastian Reiter, Alexander Viehl, Oliver Bringmann
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arXiv Computation and Language
Sep 24

UniDataAgent: An Ontology-Grounded Agent for Enterprise Question-to-Report Automation

UniDataAgent (UniDataAgent) is an ontology‑grounded system designed to automate enterprise question‑to‑report tasks while preserving organization‑specific semantics. It separates semantic acquisition from online execution, with an Ontology Acquisition and Validation (OAV) stage that builds versioned ontologies from metadata, business knowledge, and expert input, and a Question‑to‑Report Execution (QRE) stage that retrieves semantic contracts, coordinates skills and data tools, validates results, and produces evidence‑linked reports. In a deployment across 27 enterprise tables and thousands of metric types, ontology construction took a few hours versus a week manually, and report generation took minutes versus several working days, achieving 95.0% strict accuracy on real business questions compared to 72.5% for document RAG.

By Yutai Duan, Yahui Zhao, Zhangti Li, Yu Ma, Zhenfeng Qi, Shaoyang Yuan, Jing Fan, Jie Liu
ragagents
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arXiv AI
Jun 29

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications

arXiv:2606. 28070v1 Announce Type: new Abstract: JD.

By Oxygen AIIC, Chan Long, Chao Liu, Chaofan Chen, Chaohui Dong, Chunyuan Guo, Danping Liu, Debin Liu, Deping Xiang, Fulai Xu, Guangyue Liu, Hao Li, Huichun Hu, Jian Yang, Jianan Wang, Jianbo Zhao, Jiaoyang Li, Jiaxing Wang, Jinglong Li, Jinjin Guo, Jun Fang, Jun Liu, Kai Zhou, Li Wang, Lili Gao, Liying Chen, Luning Yang, Mengdi Zhou, Pengzhang Liu, Qi Lv, Qianyun Wang, Qixia Jiang, Ruyue Li, Shimu Liang, Shuxing Wang, Sijie Zhang, Siqi Li, Tianhao Gao, Wang Ke, Weihu Huang, Wencan Lai, Wenjie Zhang, Xiaohui Zhang, Xiaojing Dong, Ya Liu, Yifeng Zhang, Yixiang Wang, Yongtai Zhang, Yongyi Liao, Zhaoru Chen, Zhen Chen, Zhiyong Ma, Zhiyuan Liu, Zhongwei Liu, Ziyan Xing
llmsragefficiencymultimodal
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