TY - CHAP A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - Multi-attribute relation extraction (MARE): simplifying the application of relation extraction T2 - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA N2 - 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. Y1 - 2021 SN - 978-989-758-526-5 U6 - http://dx.doi.org/10.5220/0010559201480156 N1 - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, DeLTA2021, July 7-9, 2021 SP - 148 EP - 156 ER - TY - CHAP A1 - Kohl, Philipp A1 - Freyer, Nils A1 - Krämer, Yoka A1 - Werth, Henri A1 - Wolf, Steffen A1 - Kraft, Bodo A1 - Meinecke, Matthias A1 - Zündorf, Albert ED - Conte, Donatello ED - Fred, Ana ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP T2 - Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science N2 - 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. KW - Active learning KW - Query learning KW - Natural language processing KW - Deep learning KW - Reproducible research Y1 - 2023 SN - 978-3-031-39058-6 (Print) SN - 978-3-031-39059-3 (Online) U6 - http://dx.doi.org/978-3-031-39059-3 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 235 EP - 253 PB - Springer CY - Cham ER - TY - CHAP A1 - Kohl, Philipp A1 - Schmidts, Oliver A1 - Klöser, Lars A1 - Werth, Henri A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - STAMP 4 NLP – an agile framework for rapid quality-driven NLP applications development T2 - Quality of Information and Communications Technology. QUATIC 2021 N2 - 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. KW - Machine learning KW - Process model KW - Natural language processing Y1 - 2021 SN - 978-3-030-85346-4 SN - 978-3-030-85347-1 U6 - http://dx.doi.org/10.1007/978-3-030-85347-1_12 N1 - International Conference on the Quality of Information and Communications Technology, QUATIC 2021, 8-11 September, Algarve, Portugal SP - 156 EP - 166 PB - Springer CY - Cham ER - TY - CHAP A1 - Kraft, Bodo T1 - Conceptual design tools for civil engineering N2 - 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. KW - CAD KW - CAD KW - Bauingenieurwesen KW - CAD KW - civil engineering Y1 - 2004 ER - TY - JOUR A1 - Kraft, Bodo T1 - Conceptual design mit ArchiCAD 8 : Forschungsprojekt an der RWTH Aachen N2 - Projektbericht in GraphisoftNews - Architektur und Bauen in einer vernetzten Welt 3/2003 4 Seiten KW - CAD KW - CAD KW - Bauingenieurwesen KW - Architektur KW - CAD KW - civil engineering KW - architecture Y1 - 2003 ER - TY - GEN A1 - Kraft, Bodo T1 - LexiCAD Step by Step : Bürogebäude : Erstellen eines Grundrisses mit RoomObjects und LexiCAD N2 - 11 Seiten, 22 Abbildungen 1. Konstruktion des Außenumrisses 2. Festlegung der inneren Räume 3. Einfügen der RoomLinks 4. Wallgenerator KW - CAD KW - CAD KW - Architektur KW - CAD KW - architecture Y1 - 2003 ER - TY - BOOK A1 - Kraft, Bodo T1 - Semantische Unterstützung des konzeptuellen Gebäudeentwurfs Y1 - 2007 SN - 978-3-8322-6045-3 N1 - Berichte aus der Informatik ; Zugl.: Aachen, Techn. Hochsch., Diss., 2007 PB - Shaker CY - Aachen ER - TY - JOUR A1 - Kraft, Bodo A1 - Heer, Thomas A1 - Retkowitz, Daniel T1 - Algorithm and Tool for Ontology Integration Based on Graph Rewriting / Heer, Thomas ; Retkowitz, Daniel ; Kraft, Bodo JF - Applications of Graph Transformations with Industrial Relevance / Third International Symposium, AGTIVE 2007, Kassel, Germany, October 10-12, 2007, Revised Selected and Invited Papers Y1 - 2008 SN - 978-3-540-89019-5 N1 - Lecture Notes in Computer Science ; 5088 SP - 577 EP - 582 ER - TY - JOUR A1 - Kraft, Bodo A1 - Heer, Thomas A1 - Retkowitz, Daniel T1 - Incremental Ontology Integration / Heer, Thomas ; Retkowitz, Daniel ; Kraft, Bodo JF - 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 José Cordeiro ...] Y1 - 2008 N1 - International Conference on Enterprise Information Systems ; (10 : ; 2008.06.12-16 : ; Barcelona) ; ICEIS ; (10 : ; 2008.06.12-16 : ; Barcelona) SP - 13 EP - 28 PB - INSTICC CY - Setubal ER - TY - CHAP A1 - Kraft, Bodo A1 - Kohl, Philipp A1 - Meinecke, Matthias ED - Bernert, Christian ED - Scheurer, Steffen ED - Wehnes, Harald T1 - Analyse und Nachverfolgung von Projektzielen durch Einsatz von Natural Language Processing T2 - KI in der Projektwirtschaft : was verändert sich durch KI im Projektmanagement? Y1 - 2024 SN - 978-3-3811-1132-9 (Online) SN - 978-3-3811-1131-2 (Print) U6 - http://dx.doi.org/10.24053/9783381111329 SP - 157 EP - 167 PB - UVK Verlag ER -