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Knowledge Graphs (KGs) evolve over time and it is critical to ensure that their integrity constraints are maintained after each update. We introduce GEV, the first tool to statically ensure that a KG update in Java preserves satisfaction of SHACL constraints. This allows verification of updates at design time, and eliminates the need for costly continuous revalidation. GEV is a command-line system that loads and verifies updates, applies them to a loaded KG, and keeps track of the validation status. Internally, it relies on SHACL graph updates, a theoretical framework with a method for static verification.
The Digital Roads project at Cambridge University is leveraging digitalisation, automation, and low-carbon materials to build an Automated Maintenance Plant (AMP) for UK road networks, aimed at minimising repair times to reduce congestion, improving safety, and contributing to the UK’s net-zero goals through faster, more accurate, and efficient road maintenance.
We present PERELMAN (PipEline foR sciEntific Literature Meta-ANalysis), an agentic framework designed to extract specific information from a large corpus of scientific articles to support large-scale literature reviews and meta-analyses. Our central goal is to reliably transform heterogeneous article content into a unified, machine-readable representation. PERELMAN first elicits domain knowledge-including target variables, inclusion criteria, units, and normalization rules-through a structured dialogue with a subject-matter expert. This domain knowledge is then reused across multiple stages of the pipeline and guides coordinated agents in extracting evidence from narrative text, tables, and figures, enabling consistent aggregation across studies. In order to assess reproducibility and validate our implementation, we evaluate the system on the task of reproducing the meta-analysis of layered Li-ion cathode properties LiNi0.8Mn0.1Co0.1O2 (NMC811). We describe our solution, which has the potential to reduce the time required to prepare meta-analyses from months to minutes.
LLARS: Enabling Domain Expert & Developer Collaboration for LLM Prompting, Generation and Evaluation
We demonstrate LLARS, an open-source platform that bridges the gap between domain experts and developers for building LLM-based systems. LLARS integrates three tightly connected modules into an end-to-end pipeline: Collaborative Prompt Engineering for real-time co-authoring with version control and instant LLM testing, Batch Generation for configurable output production across user-selected prompts x models x data with cost control and Hybrid Evaluation where human and LLM evaluators jointly assess outputs through diverse assessment methods, with live agreement metrics and provenance analysis to identify the best model-prompt combination for a given use case. New prompts and models are automatically available for batch generation and completed batches can be turned into evaluation scenarios with a single click. Interviews with six domain experts and three developers in online counselling confirmed that LLARS feels intuitive, saves considerable time by keeping everything in one place and makes interdisciplinary collaboration seamless. Source code: github.com/th-nuernberg/llars