Laurenzi, Emanuele

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Laurenzi
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Emanuele
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Laurenzi, Emanuele

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2016 - 201992020 - 202420

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  • Vorschaubild
    Publikation
    Large language models: Expectations for semantics-driven systems engineering
    (Elsevier, 2024) Buchmann, Robert; Eder, Johann; Fill, Hans-Georg; Frank, Ulrich; Karagiannis, Dimitris; Laurenzi, Emanuele; Mylopoulos, John; Plexousakis, Dimitris; Santos, Maribel Yasmina
    The hype of Large Language Models manifests in disruptions, expectations or concerns in scientific communities that have focused for a long time on design-oriented research. The current experiences with Large Language Models and associated products (e.g. ChatGPT) lead to diverse positions regarding the foreseeable evolution of such products from the point of view of scholars who have been working with designed abstractions for most of their careers - typically relying on deterministic design decisions to ensure systems and automation reliability. Such expectations are collected in this paper in relation to a flavor of systems engineering that relies on explicit knowledge structures, introduced here as “semantics-driven systems engineering”. The paper was motivated by the panel discussion that took place at CAiSE 2023 in Zaragoza, Spain, during the workshop on Knowledge Graphs for Semantics-driven Systems Engineering (KG4SDSE). The workshop brought together Conceptual Modeling researchers with an interest in specific applications of Knowledge Graphs and the semantic enrichment benefits they can bring to systems engineering. The panel context and consensus are summarized at the end of the paper, preceded by a proposed research agenda considering the expressed positions.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Kein Vorschaubild vorhanden
    Publikation
    A decision-support approach for university incubators
    (Springer, 2024) Laurenzi, Emanuele; Meyer, Dario; Moesch, Patrick; Hinkelmann, Knut; Smuts, Hanlie
    04A - Beitrag Sammelband
  • Kein Vorschaubild vorhanden
    Publikation
    An LLM-aided Enterprise Knowledge Graph (EKG) engineering process
    (Stanford University, 2024) Laurenzi, Emanuele; Mathys, Adrian; Martin, Andreas; Petrick, Ron; Geib, Christopher
    Conventional knowledge engineering approaches aiming to create Enterprise Knowledge Graphs (EKG) still require a high level of manual effort and high ontology expertise, which hinder their adoption across industries. To tackle this issue, we explored the use of Large Language Models (LLMs) for the creation of EKGs through the lens of a design-science approach. Findings from the literature and from expert interviews led to the creation of the proposed artefact, which takes the form of a six-step process for EKG development. Scenarios on how to use LLMs are proposed and implemented for each of the six steps. The process is then evaluated with an anonymised data set from a large Swiss company. Results demonstrate that LLMs can support the creation of EKGs, offering themselves as a new aid for knowledge engineers.
    04B - Beitrag Konferenzschrift
  • Kein Vorschaubild vorhanden
    Publikation
    An ontology-based meta-modelling approach for semantic-driven building management systems
    (Springer, 2024) Laurenzi, Emanuele; Allan, James; Campos Macias-Hammel, Nathalie; Stoller , Sascha; Almeida, João Paulo A.; Di Ciccio, Claudio; Kalloniatis, Christos
    The increase in smart buildings has led to an increase in data produced and consumed by buildings. Despite growing digitalisation trends, data interoperability, data quality, and a lack of transparency hinder the development of scalable energy applications. Knowledge graphs alleviate some of these challenges through their ability to integrate and analyse diverse data sources. Despite these benefits, knowledge graphs require specific skills typically uncommon in building and energy system engineers. This work tackles this challenge by enabling system engineers to create and maintain knowledge graphs about BMS by dealing with visual diagrammatical models they are familiar with. For this, we built on the ontology-based meta-modelling approach and created a proof-of-concept AOAME4BMS, in which we implemented a BMS and used it for evaluation purposes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
    04B - Beitrag Konferenzschrift
  • Kein Vorschaubild vorhanden
    Publikation
    A knowledge graph-based decision support system for resilient supply chain networks
    (Springer, 2024) Düggelin, Wilhelm; Laurenzi, Emanuele; Araújo, João; de la Vara, Jose Luis; Santos, Maribel Yasmina; Assar, Saïd
    Events in recent years such as the Russo-Ukrainian war of 2022 and the covid-19 pandemic have once again shown the importance of relying on resilient supply chain networks. The creation and maintenance of such networks is, however, a rather knowledge intensive task, which is still challenging. To tackle this, we introduce a first version of a knowledge graph-based decision support system aiming to help supply chain risk managers to make sourcing decisions. The system was designed by following the design science research methodology, which is supplemented with the Ontology Development 101 [25] for rigor in creation of the knowledge graph schema. Competency questions elicited with domain experts were used to evaluate the proposed system.
    04B - Beitrag Konferenzschrift
  • Kein Vorschaubild vorhanden
    Publikation
    Visualisierung von Mustern für hybrides Lernen und Reasoning mit menschlicher Beteiligung
    (Springer, 2023) Witschel, Hans Friedrich; Pande, Charuta; Martin, Andreas; Laurenzi, Emanuele; Hinkelmann, Knut; Dornberger, Rolf
    04A - Beitrag Sammelband
  • Vorschaubild
    Publikation
    A hybrid intelligent approach combining machine learning and a knowledge graph to support academic journal publishers addressing the Reviewer Assignment Problem (RAP)
    (Sun SITE, Informatik V, RWTH Aachen, 2023) Rordorf, Dietrich Hans-Paul; Käser, Josua; Crego Corot, Alfredo Etienne; Laurenzi, Emanuele; Martin, Andreas; Fill, Hans-Georg; Gerber, Aurona; Hinkelmann, Knut; Lenat, Doug; Stolle, Reinhard; Harmelen, Frank
    This paper presents a hybrid intelligent approach that combines natural language processing (NLP) and knowledge engineering to address the Reviewer Assignment Problem (RAP) in scientific peer-review. The approach uses NLP techniques to match a new document with subject experts, and it employs a knowledge graph to identify conflicts of interest (COIs) between the authors of a document and potential reviewers. The approach detects three types of COIs: direct co-authorship, second-level coauthorship, and collaborators from the same institutions. Further, it uses semantic text similarity (STS) matching for peer-reviewing of documents in journals, where potential reviewers are screened from large literature databases. The research approach follows the Design Science Research methodology, where a prototypical system is designed based on the requirements elicited from both the literature and from primary data collection conducted in a publishing house. The approach is evaluated by implementing real-world use cases in the working prototype and by conducting a focus group with potential users, i.e., editors. © 2023 CEUR-WS. All rights reserved.
    04B - Beitrag Konferenzschrift
  • Kein Vorschaubild vorhanden
    Publikation
    Explainable AI for the olive oil industry
    (Springer, 2023) Schmid, Christian; Laurenzi, Emanuele; Michelucci, Umberto; Venturini, Francesca; Hinkelmann, Knut; López-Pellicer, Francisco J.; Polini, Andrea
    Understanding Machine Learning results for the quality assessment of olive oil is hard for non-ML experts or olive oil producers. This paper introduces an approach for interpreting such results by combining techniques of image recognition with knowledge representation and reasoning. The Design Science Research strategy was followed for the creation of the approach. We analyzed the ML results of fluorescence spectroscopy and industry-specific characteristics in olive oil quality assessment. This resulted in the creation of a domain-specific knowledge graph enriched by object recognition and image classification results. The approach enables automatic reasoning and offers explanations about fluorescence image results and, more generally, about the olive oil quality. Producers can trace quality attributes and evaluation criteria, which synergizes computer vision and knowledge graph technologies. This approach provides an applicable foundation for industries relying on fluorescence spectroscopy and AI for quality assurance. Further research on image data processing and on end-to-end automation is necessary for the practical implementation of the approach.
    04B - Beitrag Konferenzschrift
  • Kein Vorschaubild vorhanden
    Publikation
    An approach for knowledge graphs-based user stories in agile methodologies
    (Springer, 2023) Mancuso, Marco Carmelo; Laurenzi, Emanuele; Hinkelmann, Knut; López-Pellicer, Francisco J.; Polini, Andrea
    In this paper, we present AOAME4UserStories, a modelling and ontology-based approach that enables the creation of visual user stories grounded in a knowledge graph. The approach includes an ontology-based domain-specific modelling language - User Story Modelling & Notation (USMN) - and resolves the problem of creating inconsistent user stories in agile software development methodologies such as Scrum. The Design Science Research methodology was followed for the creation of USMN and its implementation in the modelling tool AOAME. The evaluation was conducted by first creating a visual user story reflecting a real-world use case and then by proving the consistent production of knowledge graphs for the given visual story.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Comparison of general-purpose and domain-specific modeling languages in the IoT domain: A case study from the OMiLAB community
    (Sun SITE, Informatik V, RWTH Aachen, 2023) Fedeli, Arianna; Beutling, Nils; Laurenzi, Emanuele; Polini, Andrea; Morichetta, Andrea; Buchmann, Robert Andrei; Sandkuhl, Kurt; Seigerroth, Ulf; Kirikova, Marite; Møller, Charles; Forbrig, Peter; Gutschmidt, Anne; Ghiran, Ana-Maria; Marcelletti, Alessandro; Härer, Felix; Re, Barbara; Johansson, Björn
    The Internet of Things (IoT) is a revolutionary concept that has rapidly transformed how we interact with technology and the world around us. In response to the inherent complexity and heterogeneity of the IoT domain, there has been a surge in the development of modeling languages and supporting platforms for developing IoT applications. Among the many modeling options available, one can distinguish between General-Purpose Modeling Languages (GPML) and Domain-Specific Modeling Languages (DSML). Each language has unique characteristics, offering distinct levels of abstraction and expressiveness crucial for effective IoT solution modeling. The challenge of selecting the most suitable language remains, with developers needing to weigh the benefits and drawbacks of each option carefully. This paper compares GPML and DSML regarding their characteristics, benefits, and drawbacks. By identifying key factors to consider when choosing a modeling language for IoT solutions, this research aims to provide valuable insights for a decision-making framework to help practitioners with this choice. To validate the findings and practical implications, a practical workshop was conducted. After creating a smart room scenario using the X-IoT DSML, the participants confirmed the advantages of DSML regarding user-friendliness, higher abstraction, improved communication, faster development, and the ability for non-experts to participate in the IoT application development process.
    04B - Beitrag Konferenzschrift