@article{KraftHeerRetkowitz2008,
  author    = {Kraft, Bodo and Heer, Thomas and Retkowitz, Daniel},
  title     = {Incremental Ontology Integration / Heer, Thomas ; Retkowitz, Daniel ; Kraft, Bodo},
  series = {Proceedings of the 10th International Conference on Enterprise Information Systems : Barcelona, Spain, June 12 - 16, 2008 / organized by INSTICC, Institute for Systems and Technologies of Information, Control and Communication ... [Ed. by Jos{\´e} Cordeiro ...]},
  journal   = {Proceedings of the 10th International Conference on Enterprise Information Systems : Barcelona, Spain, June 12 - 16, 2008 / organized by INSTICC, Institute for Systems and Technologies of Information, Control and Communication ... [Ed. by Jos{\´e} Cordeiro ...]},
  publisher = {INSTICC},
  address   = {Setubal},
  pages     = {13 -- 28},
  year      = {2008},
  language  = {en}
}
@inproceedings{Kraft2004,
  author    = {Kraft, Bodo},
  title     = {Conceptual design tools for civil engineering},
  year      = {2004},
  abstract  = {Applications of Graph Transformations with Industrial Relevance Lecture Notes in Computer Science, 2004, Volume 3062/2004, 434-439, DOI: http://dx.doi.org/10.1007/978-3-540-25959-6_33 This paper gives a brief overview of the tools we have developed to support conceptual design in civil engineering. Based on the UPGRADE framework, two applications, one for the knowledge engineer and another for architects allow to store domain specific knowledge and to use this knowledge during conceptual design. Consistency analyses check the design against the defined knowledge and inform the architect if rules are violated.},
  subject      = {CAD},
  language  = {en}
}
@article{Kraft2003,
  author    = {Kraft, Bodo},
  title     = {Conceptual design mit ArchiCAD 8 : Forschungsprojekt an der RWTH Aachen},
  year      = {2003},
  abstract  = {Projektbericht in GraphisoftNews - Architektur und Bauen in einer vernetzten Welt 3/2003 4 Seiten},
  subject      = {CAD},
  language  = {de}
}
@misc{Kraft2003,
  author    = {Kraft, Bodo},
  title     = {LexiCAD Step by Step : B{\"u}rogeb{\"a}ude : Erstellen eines Grundrisses mit RoomObjects und LexiCAD},
  year      = {2003},
  abstract  = {11 Seiten, 22 Abbildungen 1. Konstruktion des Außenumrisses 2. Festlegung der inneren R{\"a}ume 3. Einf{\"u}gen der RoomLinks 4. Wallgenerator},
  subject      = {CAD},
  language  = {de}
}
@book{Kraft2007,
  author    = {Kraft, Bodo},
  title     = {Semantische Unterst{\"u}tzung des konzeptuellen Geb{\"a}udeentwurfs},
  publisher = {Shaker},
  address   = {Aachen},
  isbn      = {978-3-8322-6045-3},
  pages     = {VIII, 381 S. : Ill., graph. Darst.},
  year      = {2007},
  language  = {de}
}
@inproceedings{KohlSchmidtsKloeseretal.2021,
  author    = {Kohl, Philipp and Schmidts, Oliver and Kl{\"o}ser, Lars and Werth, Henri and Kraft, Bodo and Z{\"u}ndorf, Albert},
  title     = {STAMP 4 NLP - an agile framework for rapid quality-driven NLP applications development},
  series = {Quality of Information and Communications Technology. QUATIC 2021},
  booktitle = {Quality of Information and Communications Technology. QUATIC 2021},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-85346-4},
  doi       = {10.1007/978-3-030-85347-1_12},
  pages     = {156 -- 166},
  year      = {2021},
  abstract  = {The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.},
  language  = {en}
}
@inproceedings{KohlFreyerKraemeretal.2023,
  author    = {Kohl, Philipp and Freyer, Nils and Kr{\"a}mer, Yoka and Werth, Henri and Wolf, Steffen and Kraft, Bodo and Meinecke, Matthias and Z{\"u}ndorf, Albert},
  title     = {ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP},
  series = {Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science},
  booktitle = {Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science},
  editor    = {Conte, Donatello and Fred, Ana and Gusikhin, Oleg and Sansone, Carlo},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-39058-6 (Print)},
  doi       = {978-3-031-39059-3},
  pages     = {235 -- 253},
  year      = {2023},
  abstract  = {Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.},
  language  = {en}
}
@inproceedings{KloeserKohlKraftetal.2021,
  author    = {Kl{\"o}ser, Lars and Kohl, Philipp and Kraft, Bodo and Z{\"u}ndorf, Albert},
  title     = {Multi-attribute relation extraction (MARE): simplifying the application of relation extraction},
  series = {Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA},
  booktitle = {Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA},
  isbn      = {978-989-758-526-5},
  doi       = {10.5220/0010559201480156},
  pages     = {148 -- 156},
  year      = {2021},
  abstract  = {Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.},
  language  = {en}
}
@inproceedings{KloeserBuesgenKohletal.2023,
  author    = {Kl{\"o}ser, Lars and B{\"u}sgen, Andr{\´e} and Kohl, Philipp and Kraft, Bodo and Z{\"u}ndorf, Albert},
  title     = {Explaining relation classification models with semantic extents},
  series = {DeLTA 2023: Deep Learning Theory and Applications},
  booktitle = {DeLTA 2023: Deep Learning Theory and Applications},
  editor    = {Conte, Donatello and Fred, Ana and Gusikhin, Oleg and Sansone, Carlo},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-39058-6 (Print)},
  doi       = {10.1007/978-3-031-39059-3_13},
  pages     = {189 -- 208},
  year      = {2023},
  abstract  = {In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.},
  language  = {en}
}
@article{KirchhofKraft2012,
  author    = {Kirchhof, Michael and Kraft, Bodo},
  title     = {Hybrides Vorgehensmodell : Agile und klassische Methoden im Projekt passend kombinieren},
  series = {ProjektMagazin},
  journal   = {ProjektMagazin},
  number    = {11},
  publisher = {Berleb Media},
  address   = {Taufkirchen},
  pages     = {11 S.},
  year      = {2012},
  abstract  = {Agil ist im Trend und immer mehr Unternehmen, die ihre Projekte bisher nach klassischen Prinzipien durchf{\"u}hrten, denken {\"u}ber den Einsatz agiler Methoden nach. Doch selbst wenn die Organisation bereits beide Philosophien unterst{\"u}tzt, gilt f{\"u}r ein Projekt meist die klare Vorgabe: agil oder klassisch. Es gibt aber noch einen anderen Ansatz, mit diesen "unterschiedlichen Welten" umzugehen: Und zwar die beiden Philosophien innerhalb eines Projekts zu kombinieren. Wie dies in der Praxis aussehen und gelingen kann, zeigen Dr. Michael Kirchhof und Prof. Dr. Bodo Kraft in diesem Beitrag.},
  language  = {de}
}