ReCAP Trier University
Logo of the project
 Workshops

Workshop on Text Mining and Generation (TMG) @ ICCBR 2023

  •   July 17, 2023
  •   Full-day
  •   Co-located with ICCBR 2023
  •   Aberdeen, Scotland

Digital text data is produced across different sources such as social media. Simultaneously, very often only structured data is available. Within CBR, cases of the former are usually handled by using methods of Textual CBR, while Process-Oriented CBR addresses on the latter type of data. By leveraging their generic research origins, i.e., text mining and text generation approaches, we aim to diminish this gap. The target of text mining is to extract (useful) structured information from unstructured text. In contrast, text generation attempts to (automatically) create text from structured information or distributed knowledge. The goal of the TMG workshop is to bring these two perspectives together by eliciting research paper submissions that aim for applying text mining and generation approach in the context of CBR. We welcome any submission from any domain aiming to contribute to to close this gap.

Workshop Schedule and Accepted Papers

The TMG workshop will be held together with the BEAR workshop. Each accepted paper will have 15–20 minutes for presentation and 10 minutes for discussion. The proceedings are available at CEUR.

StartEndEvent
11:3011:40Opening
11:4013:00Keynote by Prof. Dr. Chris Reed with discussion
13:0014:00Lunch break
14:0015:30Session 1: BEAR papers
  • Florian Brand, Katharina Lott, Lukas Malburg, Maximilian Hoffmann, Ralph Bergmann

    Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows

     Published paper

  • Lukas Malburg, Alexander Schultheis, Ralph Bergmann

    Modeling and Using Complex IoT Time Series Data in Case-Based Reasoning: From Application Scenarios to Implementations

     Published paper

  • Joseph Kendall-Morwick

    Working with Ambiguous Case Representations

     Published paper

15:3016:00Coffee break
16:0017:30Session 2: TMG papers
  • Markus Nilles, Lorik Dumani, Björn Metzler, Ralf Schenkel

    Trust me, I am an Expert: Predicting the Credibility of Experts for Statements

     Published paper

  • Marcel Lamott, Jörn Hees, Adrian Ulges

    Knowledge Base Question Answering by Transformer-Based Graph Pattern Scoring

     Published paper

  • Mircea-Luchian Pojoni, Lorik Dumani, Ralf Schenkel

    Argument-Mining from Podcasts Using ChatGPT

     Published paper

17:3018:00Invited talk

Zonglin Yang, Xinya Du, Erik Cambria, Claire Cardie

End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion

 Published paper

Keynote by Prof. Dr. Chris Reed

Prof. Dr. Chris Reed

Chris Reed is Professor of Computer Science and Philosophy at the University of Dundee in Scotland, where he heads the Centre for Argument Technology. Chris has been working at the overlap between argumentation theory and artificial intelligence for two decades and specialises in the theory, practice and commercialisation of argument technology. He has won over £6.5m of funding from government, charity and commercial sources, has over 200 peer-reviewed papers in the area including five books, and has served as a director of several technology companies.

Important Dates

All dates are calculated at 11:59 pm UTC
DateDescription
June 2, 2023Paper submission
June 16, 2023Paper notification
June 26, 2023Camera-ready copy
July 17, 2023Workshop date

Call for Papers

We welcome any submissions that deal with transforming the representation of data between structured and unstructured formats in the context of CBR-based systems: (applied) research papers, theoretical papers, user studies or prospective papers. Topics include, but are not limited to, the following:

  • Text mining for argumentation.
  • Case-based knowledge representation of text.
  • Similarity-based retrieval and ranking.
  • Informed similarity measures for structured text.
  • Generating descriptions for graph-based argument case representations.
  • Generating case-based explanations for retrieval.
  • Methods for Explainable CBR.
  • Ethical aspects of AI for text generation (e.g., mitigating bias or misinformation).
  • Integration of background knowledge and machine learning.
  • CBR and knowledge graphs.
  • Graph-to-text generation with knowledge graphs.
  • Knowledge graph refinement, particularly featuring text-based signals.
  • Snippet generation for search results.
  • CBR for Deep Learning with Text.

Submission Information

The submission of the papers should be in accordance to the CEUR-WS style and have to be submitted via EasyChair. Please select the track W4 - Text Mining and Generation. Authors can submit the following types of papers:

  • Full Paper (10–16 pages, including references)
  • Short Paper (5–9 pages, including references)

At least one author of each accepted paper must register for the workshop and present the contribution.

Organizing Committee

Mirko Lenz

Mirko Lenz

 info@mirko-lenz.de

 Trier University

Lorik Dumani

Lorik Dumani

 dumani@uni-trier.de

 Trier University

Premtim Sahitaj

Premtim Sahitaj

 sahitaj@uni-trier.de

 Trier University

Jordan Robinson

Jordan Robinson

 J.Robinson9@liverpool.ac.uk

 University of Liverpool

Program Committee

  • Alexander Bondarenko (Jena University)
  • Wei-Fan Chen (Paderborn University)
  • Philipp Heinisch (Bielefeld University)
  • Prof. Dr. Achim Rettinger (Trier University)
  • Prof. Dr. Lutz Schröder (Friedrich-Alexander-Universität Erlangen-Nürnberg)
  • Prof. Dr. Adrian Ulges (RheinMain University of Applied Sciences)

© 2024 Trier University

ImprintPrivacy Policy