Aerospace manufacturing companies, such as Thales Alenia Space, design, develop, integrate, verify, and validate products characterized by high complexity and low volume. They carefully document all phases for each product but analyses across products are challenging due to the heterogeneity and unstructured nature of the data in documents. In this paper, we propose a hybrid methodology that leverages Knowledge Graphs (KGs) in conjunction with Large Language Models (LLMs) to extract and validate data contained in these documents. We consider a case study focused on test data related to electronic boards for satellites. To do so, we extend the Semantic Sensor Network ontology. We store the metadata of the reports in a KG, while the actual test results are stored in parquet accessible via a Virtual Knowledge Graph. The validation process is managed using an LLM-based approach. We also conduct a benchmarking study to evaluate the performance of state-of-the-art LLMs in executing this task. Finally, we analyze the costs and benefits of automating preexisting processes of manual data extraction and validation for subsequent cross-report analyses.
Pre-trained large language models have recently achieved ground-breaking performance in a wide variety of language understanding tasks. However, the same model can not be applied to multimodal behavior understanding tasks (e.g., video sentiment/humor detection) unless non-verbal features (e.g., acoustic and visual) can be integrated with language. Jointly modeling multiple modalities significantly increases the model complexity, and makes the training process data-hungry. While an enormous amount of text data is available via the web, collecting large-scale multimodal behavioral video datasets…
Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces
University of Stuttgart（斯图加特大学）；University of Bonn（波恩大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；TU Dresden（德累斯顿工业大学）；Amazon（亚马逊）；University of Southampton（南安普顿大学）
TextualRL: Textual Reinforcement Learning for Context Management
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
University of Science and Technology of China（中国科学技术大学）；Microsoft Research Asia（微软亚洲研究院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Microsoft India Development Center（微软印度开发中心）
Integrating Expert Knowledge into Logical Programs via LLMs
Multimedia Systems Department, Gdansk University of Technology, Poland（格但斯克技术大学多媒体系统系，波兰）；Department of Computer Science, University of Manchester, United Kingdom（曼彻斯特大学计算机科学系，英国）；National Biomarker Centre (NBC), CRUK Manchester Institute, United Kingdom（英国CRUK曼彻斯特研究所国家生物标记中心）；Idiap Research Institute, Martigny, Switzerland（瑞士马尔蒂尼Idiap研究所）
Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting
School of Software Engineering Sun Yat-Sen University（软件工程学院中山大学）；Institute of Data Science（数据科学研究院）；School of Computing（计算机学院）；National University of Singapore（新加坡国立大学）
Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision
Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）；Peking University（北京大学）；School of Information, Renmin University of China（中国人民大学信息学院）；Harbin Institute of Technology（哈尔滨工业大学）；North China Electric Power University（华北电力大学）；GDS Holdings Limited（GDS控股有限公司）
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine
University of Connecticut（康涅狄格大学）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；School of Computing, and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；UMass Chan Medical School（马萨诸塞大学陈医学院）；University of Minnesota（明尼苏达大学）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；Optum AI；University of Massachusetts, Lowell（马萨诸塞大学洛厄尔分校）
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
University of New Brunswick（新不伦瑞克大学）；Quy Nhon AI, FPT Software（FPT软件公司归仁人工智能部门）；Friedrich-Alexander-University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Software AG（软件AG公司）
LangXAI 框架将 XAI 与先进视觉模型结合，为分类、检测和分割任务生成文本解释，以高 BERTScore 验证其合理性，从而提升终端用户对视觉 AI 的理解与信任。
University of Aberdeen（阿伯丁大学）；The University of Sydney（悉尼大学）
This paper proposes an enhanced natural language generation system combining the merits of both rule-based approaches and modern deep learning algorithms, boosting its performance to the extent where the generated textual content is capable of exhibiting agile human-writing styles and the content logic of which is highly controllable. We also come up with a novel approach called HMCU to measure the performance of the natural language processing comprehensively and precisely.
Research Centre on Information Technologies (CiTIUS), University of Santiago de Compostela（圣地亚哥-德孔波斯特拉大学信息技术研究中心（CiTIUS））；MeteoGalicia（加利西亚气象局）
We present in this paper an application which automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic description of data together with a natural language generation (NLG) system. The application, named GALiWeather, extracts relevant information from weather forecast input data and encodes it into intermediate descriptions using linguistic variables and t…
Enhancing textual textbook question answering with large language models and retrieval augmented generation
Faculty of Computing and Information Technology, King Abdulaziz University（阿卜杜勒阿齐兹国王大学计算与信息技术学院）；College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学计算机与信息科学学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）
Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
Fraunhofer IAIS - Department of Media Engineering（弗劳恩霍夫研究所媒体工程部门）；University of Bonn - Department of Computer Science（波恩大学计算机科学系）；West-AI - Federal Ministry of Education and Research（西德人工智能 - 教育与研究部）
EndoCogniAgent: Closed-Loop Agentic Reasoning with Self-Consistency Validation for Endoscopic Diagnosis
School of Biological Science and Medical Engineering, Southeast University（东南大学生物科学与医学工程学院）；Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University（江苏省生物材料与器件重点实验室）；State Key Laboratory of Digital Medical Engineering, Southeast University（国家数字医学工程重点实验室）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing（江苏省联合国际医学信息处理联合实验室）；Laboratory of Image Science and Technology, the School of Computer Science and Engineering, Southeast University（图像科学与技术实验室，东南大学计算机科学与工程学院）
University of Southern California（南加利福尼亚大学）；Google（谷歌）；Google Research（谷歌研究院）
Natural language understanding tasks such as open-domain question answering often require retrieving and assimilating factual information from multiple sources. We propose to address this problem by integrating a semi-parametric representation of a large text corpus into a Transformer model as a source of factual knowledge. Specifically, our method represents knowledge with `mention memory', a table of dense vector representations of every entity mention in a corpus. The proposed model - TOME - is a Transformer that accesses the information through internal memory layers in which each entity m…
Topology of Reasoning: Retrieved Cell Complex-Augmented Generation for Textual Graph Question Answering
Academy of Advanced Interdisciplinary Studies, Chongqing University of Posts and Telecommunications（重庆邮电大学先进交叉学科研究院）；School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Intelligent System Department, Zhongxing Telecom Equipment (ZTE)（中兴电信设备智能系统部）
