Institution profile

Karlsruhe University of Applied Sciences

Academic institutioneurope · de
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Research library14linked papers
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Selected work

Representative Papers

ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts

Aug 04, 2026

This study addresses the challenging task of automatically identifying and segmenting legal conditions (Tatbestand) from legal consequences (Rechtsfolge) in German statutory texts. To facilitate research on this structural parsing problem, the authors introduce ANNOTARES, the first fine-grained annotated dataset covering three major German legal codes, enabling cross-code generalization studies. The work systematically evaluates a range of approaches, including rule-based baselines, CRF, BiLSTM, BiLSTM-CRF, and Transformer architectures based on BERT and large language models. Experimental results demonstrate that BERT-based and large language models significantly outperform traditional methods in capturing the complex syntactic structures inherent in legal texts, thereby confirming the effectiveness of pretrained language models for extracting logical structures in legal documents.

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Agentic Self-Healing for Data and AI Pipelines: An Affordable Vendor-Agnostic Architecture using Open-Source Software

Aug 03, 2026

This work addresses the frequent disruptions in data and AI pipelines caused by data anomalies, schema changes, or infrastructure failures, which existing self-healing solutions often mitigate at high cost, with vendor lock-in, or with limited adaptability for small-to-medium teams. The paper proposes an open-source, vendor-agnostic autonomous remediation architecture that integrates monitoring, metadata, historical incident logs, a policy engine, AI-driven root cause analysis, and controlled repair mechanisms to automatically detect, diagnose, remediate, and validate pipeline issues. Its key contribution lies in delivering a unified, portable end-to-end reference architecture that systematically consolidates fragmented capabilities without reliance on proprietary platforms, substantially reducing manual intervention and enhancing pipeline resilience across diverse contexts such as data engineering, MLOps, and software delivery.

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Recent publications

Latest Papers

ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts

Aug 04, 2026

This study addresses the challenging task of automatically identifying and segmenting legal conditions (Tatbestand) from legal consequences (Rechtsfolge) in German statutory texts. To facilitate research on this structural parsing problem, the authors introduce ANNOTARES, the first fine-grained annotated dataset covering three major German legal codes, enabling cross-code generalization studies. The work systematically evaluates a range of approaches, including rule-based baselines, CRF, BiLSTM, BiLSTM-CRF, and Transformer architectures based on BERT and large language models. Experimental results demonstrate that BERT-based and large language models significantly outperform traditional methods in capturing the complex syntactic structures inherent in legal texts, thereby confirming the effectiveness of pretrained language models for extracting logical structures in legal documents.

0 citationsRead paper

Agentic Self-Healing for Data and AI Pipelines: An Affordable Vendor-Agnostic Architecture using Open-Source Software

Aug 03, 2026

This work addresses the frequent disruptions in data and AI pipelines caused by data anomalies, schema changes, or infrastructure failures, which existing self-healing solutions often mitigate at high cost, with vendor lock-in, or with limited adaptability for small-to-medium teams. The paper proposes an open-source, vendor-agnostic autonomous remediation architecture that integrates monitoring, metadata, historical incident logs, a policy engine, AI-driven root cause analysis, and controlled repair mechanisms to automatically detect, diagnose, remediate, and validate pipeline issues. Its key contribution lies in delivering a unified, portable end-to-end reference architecture that systematically consolidates fragmented capabilities without reliance on proprietary platforms, substantially reducing manual intervention and enhancing pipeline resilience across diverse contexts such as data engineering, MLOps, and software delivery.

0 citationsRead paper