The paper introduces a pipeline that merges structured disaster records from EM‑DAT with unstructured documents from ReliefWeb and the European Media Monitor to generate source‑grounded disaster storylines and causal knowledge graphs. Using Retrieval‑Augmented Generation, it produces tabular event profiles covering 17 fields and builds causal graphs enriched with citation‑grounded explanatory narratives, allowing traceability to primary sources. Human evaluation across three crisis cases shows high retrieval precision, strong faithfulness of causal relations, and a clear expert preference for citation‑grounded components over ungrounded ones.
By Ivan Decostanzi, Michele Ronco, Sergio Consoli, Christina Corbane, Lorenzo Bertolini, Indaco Biazzo, Daria Mihaila, Manuel Garcia-Herranz, Felix Schwebel, Yelena Mejova, Kyriaki Kalimeri
arXiv:2606. 31614v1 Announce Type: cross Abstract: Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency.
By Javal Vyas, Milapji Singh Gill, Mehmet Mercang\"oz
arXiv:2606. 08376v1 Announce Type: cross Abstract: As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity.
By Leihan Zhang, Wecheng Ye, Xianlong Ma, Haochuan Liu, Yang Li, Qianyu Zhang, Jinliang Chen, Qiang Yan
Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve jointly, yet they are typically studied in isolation.
arXiv:2608. 08056v1 Announce Type: new Abstract: Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular schemata introduced by institutions and (c) completely unstructured textual information data provided by healthcare professionals.
By Ioannis N. Tzortzis, Georgia Kapetadimitri, Agapi Davradou, Nefeli Kousta, Nikolaos Bakalos, Ioannis Rallis, Dimitrios Kalogeras, Nikolaos Doulamis, Anastasios Doulamis
arXiv:2608.22974v1 Announce Type: new
Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...
By Xiaohui Zhang, Zequn Sun, Chengyuan Yang, Yuanning Cui, Lingbing Guo, Wei Hu
EvoOntology introduces a self‑evolving ontology layer for data agents, encapsulating the ontology as an MCP server with schema, content, and tool layers. It enables agents to query and interact with the ontology at runtime, using a builder agent for autonomous construction and a self‑evolution loop that refines the ontology through attribution‑guided edits validated by backbone‑conditional evaluation. Experiments on three data‑agent benchmarks with four LLM backbones show that EvoOntology consistently outperforms strong baselines and existing semantic‑layer approaches, effectively bridging the agent‑data gap for heterogeneous data.
By Meiduo Chong, Shaolei Zhang, Ju Fan, Xiaoyong Du
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
The paper introduces DisCTI, a system that automatically maps cyber threat intelligence (CTI) events to relevant industry sectors using a multilabel classification approach. By creating a dataset of 872 sector‑labelled CTI events and applying a BERT transformer model, the authors achieve a macro‑averaged F1‑score of 0.89, correctly assigning 94.5% of sector labels. This demonstrates that embedding expert knowledge into machine learning can enable timely, sector‑aware CTI dissemination, improving defensive response.
By Fajar Wijitrisnanto (National Cyber and Crypto Agency, Jakarta, Indonesia), Alsharif Abuadbba (CSIRO, Sydney, Australia), Yansong Gao (CSIRO, Sydney, Australia, The University of Western Australia, Perth, Australia), Nan Wu (CSIRO, Sydney, Australia)
The paper introduces a reusable Semantic Web framework that aggregates fragmented evidence needed for Fundamental Rights Impact Assessments under the EU AI Act, focusing on high‑risk public sector categories such as employment and worker management and access to essential public services. A curated 150‑record corpus is annotated across four axes and serialized into a SPARQL‑queryable knowledge graph of 1,351 RDF triples, enabling five demonstration scenarios that retrieve 103 records (68.7% coverage). Evaluation against a 69‑record gold standard shows that LLM‑assisted classification in the employment domain yields a low κ of 0.045, highlighting challenges in automated fairness‑related evidence retrieval, while all artefacts are released openly for regulators, authorities, and SMEs.
By Faith Olopade, Delaram Golpayegani, David Lewis
arXiv:2607. 22153v1 Announce Type: cross Abstract: Industrial health management increasingly relies on heterogeneous information sources, including condition monitoring systems, supervisory control and data acquisition systems, maintenance records, inspection results, and prognostic models.
By Deshui Li, Xiao-Ming Yuan, Zishun Wang
arXiv:2608.24559v1 Announce Type: cross
Abstract: Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from moder...
By Angelo Salatino, Francesco Osborne, Alexis Vizcaino, Aliaksandr Birukou, Enrico Motta