---
_id: '60958'
abstract:
- lang: eng
  text: Large Language Models (LLMs) excel in understanding, generating, and processing
    human language, with growing adoption in process mining. Process mining relies
    on event logs that capture explicit process knowledge; however, knowledge-intensive
    processes (KIPs) in domains such as healthcare and product development depend
    on tacit knowledge, which is often absent from event logs. To bridge this gap,
    this study proposes a LLM-based framework for mobilizing tacit process knowledge
    and enriching event logs. A proof-of-concept is demonstrated using a KIP-specific
    LLM-driven conversational agent built on GPT-4o. The results indicate that LLMs
    can capture tacit process knowledge through targeted queries and systematically
    integrate it into event logs. This study presents a novel approach combining LLMs,
    knowledge management, and process mining, advancing the understanding and management
    of KIPs by enhancing knowledge accessibility and documentation.
author:
- first_name: Katharina
  full_name: Brennig, Katharina
  last_name: Brennig
citation:
  ama: 'Brennig K. Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit
    Knowledge in Event Logs of Knowledge-Intensive Processes. In: <i>AMCIS 2025 Proceedings.
    11.</i> ; 2025.'
  apa: 'Brennig, K. (2025). Revealing the Unspoken: Using LLMs to Mobilize and Enrich
    Tacit Knowledge in Event Logs of Knowledge-Intensive Processes. <i>AMCIS 2025
    Proceedings. 11.</i> Americas Conference on Information Systems, Montréal.'
  bibtex: '@inproceedings{Brennig_2025, title={Revealing the Unspoken: Using LLMs
    to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes},
    booktitle={AMCIS 2025 Proceedings. 11.}, author={Brennig, Katharina}, year={2025}
    }'
  chicago: 'Brennig, Katharina. “Revealing the Unspoken: Using LLMs to Mobilize and
    Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes.” In <i>AMCIS
    2025 Proceedings. 11.</i>, 2025.'
  ieee: 'K. Brennig, “Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit
    Knowledge in Event Logs of Knowledge-Intensive Processes,” presented at the Americas
    Conference on Information Systems, Montréal, 2025.'
  mla: 'Brennig, Katharina. “Revealing the Unspoken: Using LLMs to Mobilize and Enrich
    Tacit Knowledge in Event Logs of Knowledge-Intensive Processes.” <i>AMCIS 2025
    Proceedings. 11.</i>, 2025.'
  short: 'K. Brennig, in: AMCIS 2025 Proceedings. 11., 2025.'
conference:
  end_date: 2025-08-16
  location: Montréal
  name: Americas Conference on Information Systems
  start_date: 2025-08-14
date_created: 2025-08-20T07:03:37Z
date_updated: 2025-08-20T07:06:16Z
department:
- _id: '196'
keyword:
- Process Mining
- Large Language Model
- Knowledge Management
- Knowledge-Intensive Process
- Tacit Knowledge
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/amcis2025/sig_svc/sig_svc/11/
publication: AMCIS 2025 Proceedings. 11.
related_material:
  link:
  - relation: confirmation
    url: https://aisel.aisnet.org/amcis2025/sig_svc/sig_svc/11/
status: public
title: 'Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge
  in Event Logs of Knowledge-Intensive Processes'
type: conference
user_id: '51905'
year: '2025'
...
---
_id: '57445'
abstract:
- lang: eng
  text: Knowledge management is essential for successful disaster management. This
    paper conducts a Systematic Literature Review at the intersection of the knowledge
    management field and disaster management and examines the available body of literature.
    Fire departments are chosen as the focus group as they are the most prevalent
    emergency services. There are many publications that deal with knowledge management
    during the response phase of an emergency. Often, the literature focuses on the
    application of knowledge management in large-scale disasters to link the various
    organizations on-scene. What is missing in most approaches is a prior step of
    implementing and training the knowledge management system. Therefore, this literature
    review seeks to provide an overview of approaches for daily routines and small-to-medium
    incidents that serve as a training ground. However, literature on non-incident
    phases and smaller incidents is scarce. As information technologies are developing
    rapidly, there is no modern and recent description of the current use of knowledge
    management solutions in this area.
author:
- first_name: Andreas Maximilian
  full_name: Schultz, Andreas Maximilian
  id: '40599'
  last_name: Schultz
- first_name: Fabian
  full_name: Dotzki, Fabian
  last_name: Dotzki
- first_name: Iryna
  full_name: Mozgova, Iryna
  id: '95903'
  last_name: Mozgova
citation:
  ama: 'Schultz AM, Dotzki F, Mozgova I. Knowledge Management in Civil Protection
    at the Example of Fire Brigades. In: <i>Proceedings of the 16th International
    Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management</i>.
    SCITEPRESS - Science and Technology Publications; 2024. doi:<a href="https://doi.org/10.5220/0012947700003838">10.5220/0012947700003838</a>'
  apa: Schultz, A. M., Dotzki, F., &#38; Mozgova, I. (2024). Knowledge Management
    in Civil Protection at the Example of Fire Brigades. <i>Proceedings of the 16th
    International Joint Conference on Knowledge Discovery, Knowledge Engineering and
    Knowledge Management</i>. International Joint Conference on Knowledge Discovery,
    Knowledge Engineering and Knowledge Management, Porto, Portugal. <a href="https://doi.org/10.5220/0012947700003838">https://doi.org/10.5220/0012947700003838</a>
  bibtex: '@inproceedings{Schultz_Dotzki_Mozgova_2024, title={Knowledge Management
    in Civil Protection at the Example of Fire Brigades}, DOI={<a href="https://doi.org/10.5220/0012947700003838">10.5220/0012947700003838</a>},
    booktitle={Proceedings of the 16th International Joint Conference on Knowledge
    Discovery, Knowledge Engineering and Knowledge Management}, publisher={SCITEPRESS
    - Science and Technology Publications}, author={Schultz, Andreas Maximilian and
    Dotzki, Fabian and Mozgova, Iryna}, year={2024} }'
  chicago: Schultz, Andreas Maximilian, Fabian Dotzki, and Iryna Mozgova. “Knowledge
    Management in Civil Protection at the Example of Fire Brigades.” In <i>Proceedings
    of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering
    and Knowledge Management</i>. SCITEPRESS - Science and Technology Publications,
    2024. <a href="https://doi.org/10.5220/0012947700003838">https://doi.org/10.5220/0012947700003838</a>.
  ieee: 'A. M. Schultz, F. Dotzki, and I. Mozgova, “Knowledge Management in Civil
    Protection at the Example of Fire Brigades,” presented at the International Joint
    Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management,
    Porto, Portugal, 2024, doi: <a href="https://doi.org/10.5220/0012947700003838">10.5220/0012947700003838</a>.'
  mla: Schultz, Andreas Maximilian, et al. “Knowledge Management in Civil Protection
    at the Example of Fire Brigades.” <i>Proceedings of the 16th International Joint
    Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management</i>,
    SCITEPRESS - Science and Technology Publications, 2024, doi:<a href="https://doi.org/10.5220/0012947700003838">10.5220/0012947700003838</a>.
  short: 'A.M. Schultz, F. Dotzki, I. Mozgova, in: Proceedings of the 16th International
    Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management,
    SCITEPRESS - Science and Technology Publications, 2024.'
conference:
  end_date: 2024-11-19
  location: Porto, Portugal
  name: International Joint Conference on Knowledge Discovery, Knowledge Engineering
    and Knowledge Management
  start_date: 2024-11-17
date_created: 2024-11-27T07:50:07Z
date_updated: 2024-11-27T07:53:24Z
department:
- _id: '741'
doi: 10.5220/0012947700003838
keyword:
- Knowledge Management
- Civil Protection
- Systematic Literature Review
- Fire Brigade
language:
- iso: eng
main_file_link:
- url: https://www.scitepress.org/Link.aspx?doi=10.5220/0012947700003838
publication: Proceedings of the 16th International Joint Conference on Knowledge Discovery,
  Knowledge Engineering and Knowledge Management
publication_status: published
publisher: SCITEPRESS - Science and Technology Publications
status: public
title: Knowledge Management in Civil Protection at the Example of Fire Brigades
type: conference
user_id: '40599'
year: '2024'
...
---
_id: '57240'
abstract:
- lang: eng
  text: Validating assertions before adding them to a knowledge graph is an essential
    part of its creation and maintenance. Due to the sheer size of knowledge graphs,
    automatic fact-checking approaches have been developed. These approaches rely
    on reference knowledge to decide whether a given assertion is correct. Recent
    hybrid approaches achieve good results by including several knowledge sources.
    However, it is often impractical to provide a sheer quantity of textual knowledge
    or generate embedding models to leverage these hybrid approaches. We present FaVEL,
    an approach that uses algorithm selection and ensemble learning to amalgamate
    several existing fact-checking approaches that rely solely on a reference knowledge
    graph and, hence, use fewer resources than current hybrid approaches. For our
    evaluation, we create updated versions of two existing datasets and a new dataset
    dubbed FaVEL-DS. Our evaluation compares our approach to 15 fact-checking approaches—including
    the state-of-the-art approach HybridFC—on 3 datasets. Our results demonstrate
    that FaVEL outperforms all other approaches significantly by at least 0.04 in
    terms of the area under the ROC curve. Our source code, datasets, and evaluation
    results are open-source and can be found at https://github.com/dice-group/favel.
author:
- first_name: Umair
  full_name: Qudus, Umair
  id: '83392'
  last_name: Qudus
  orcid: 0000-0001-6714-8729
- first_name: Michael
  full_name: Röder, Michael
  id: '67199'
  last_name: Röder
  orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Franck Lionel
  full_name: Tatkeu Pekarou, Franck Lionel
  last_name: Tatkeu Pekarou
- first_name: Ana Alexandra
  full_name: Morim da Silva, Ana Alexandra
  id: '72108'
  last_name: Morim da Silva
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Qudus U, Röder M, Tatkeu Pekarou FL, Morim da Silva AA, Ngonga Ngomo A-C.
    FaVEL: Fact Validation Ensemble Learning. In: Rospocher M, Mehwish Alam, eds.
    <i>EKAW 2024</i>. ; 2024.'
  apa: 'Qudus, U., Röder, M., Tatkeu Pekarou, F. L., Morim da Silva, A. A., &#38;
    Ngonga Ngomo, A.-C. (2024). FaVEL: Fact Validation Ensemble Learning. In M. Rospocher
    &#38; Mehwish Alam (Eds.), <i>EKAW 2024</i>.'
  bibtex: '@inproceedings{Qudus_Röder_Tatkeu Pekarou_Morim da Silva_Ngonga Ngomo_2024,
    title={FaVEL: Fact Validation Ensemble Learning}, booktitle={EKAW 2024}, author={Qudus,
    Umair and Röder, Michael and Tatkeu Pekarou, Franck Lionel and Morim da Silva,
    Ana Alexandra and Ngonga Ngomo, Axel-Cyrille}, editor={Rospocher, Marco and Mehwish
    Alam}, year={2024} }'
  chicago: 'Qudus, Umair, Michael Röder, Franck Lionel Tatkeu Pekarou, Ana Alexandra
    Morim da Silva, and Axel-Cyrille Ngonga Ngomo. “FaVEL: Fact Validation Ensemble
    Learning.” In <i>EKAW 2024</i>, edited by Marco Rospocher and Mehwish Alam, 2024.'
  ieee: 'U. Qudus, M. Röder, F. L. Tatkeu Pekarou, A. A. Morim da Silva, and A.-C.
    Ngonga Ngomo, “FaVEL: Fact Validation Ensemble Learning,” in <i>EKAW 2024</i>,
    Amsterdam, Netherlands, 2024.'
  mla: 'Qudus, Umair, et al. “FaVEL: Fact Validation Ensemble Learning.” <i>EKAW 2024</i>,
    edited by Marco Rospocher and Mehwish Alam, 2024.'
  short: 'U. Qudus, M. Röder, F.L. Tatkeu Pekarou, A.A. Morim da Silva, A.-C. Ngonga
    Ngomo, in: M. Rospocher, Mehwish Alam (Eds.), EKAW 2024, 2024.'
conference:
  end_date: 2024-11-28
  location: Amsterdam, Netherlands
  name: 24th International Conference on Knowledge Engineering and Knowledge Management
  start_date: 2024-11-26
corporate_editor:
- Mehwish Alam
date_created: 2024-11-19T14:12:49Z
date_updated: 2025-09-11T09:48:12Z
ddc:
- '600'
department:
- _id: '34'
editor:
- first_name: Marco
  full_name: Rospocher, Marco
  last_name: Rospocher
file:
- access_level: closed
  content_type: application/pdf
  creator: uqudus
  date_created: 2024-11-19T14:14:14Z
  date_updated: 2024-11-19T14:14:14Z
  file_id: '57241'
  file_name: favel.pdf
  file_size: 190661
  relation: main_file
  success: 1
file_date_updated: 2024-11-19T14:14:14Z
has_accepted_license: '1'
keyword:
- fact checking
- ensemble learning
- transfer learning
- knowledge management.
language:
- iso: eng
popular_science: '1'
project:
- _id: '412'
  name: 'NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen'
- _id: '285'
  name: 'SAIL: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen
    Systemen'
- _id: '410'
  name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: EKAW 2024
quality_controlled: '1'
status: public
title: 'FaVEL: Fact Validation Ensemble Learning'
type: conference
user_id: '83392'
year: '2024'
...