2nd Workshop on Beyond Attribute-Value Case Representation (BEAR)

To address complex socio-ecological challenges, related to this year's ICCBR conference theme, “CBR for Socio-Ecological Welfare”, this workshop will investigate a deeper integration of context and temporal aspects into CBR through case representations that go beyond attribute-value case representations. A strong focus will be on practical applications of CBR that represent novel solutions to real-world challenges. By considering concrete applications of CBR, the effective use of existing knowledge in intelligent systems and thereby successfully overcoming challenges in a socio-ecological context will be emphasized. This workshop contributes to the conference theme by presenting research work based on complex case representations that promote long-term well-being in socio-ecological systems.

The 2nd BEAR workshop discusses research work, CBR applications, and systems where typical attribute-value case representations reach their limits and complex case representations such as object-oriented, textual, graph-structured, time-oriented (time series), hierarchical or hybrid representations need to be used as well as practical applications of such case representations, that imply necessary (research) challenges. In general, these challenges affect all phases of the CBR cycle. For example, the complex case representation impacts the performance of similarity-based retrieval and adaptation. In addition, it affects the use of other AI methods integrated with CBR. In this workshop, participants shall present their research with complex case representations focusing on general challenges and the impact on the CBR phases or on the application of such representations. The workshop aims to foster collaboration and exchange of ideas among researchers, developers, and others who use complex case representations that go beyond attribute-value case representations.  

Contributions should be in areas that include, but are not limited to, the following topics: 

• Representation, Authoring, Elicitation, and Visualization of Complex Case Representations (Text, Graphs, Workflows/Processes, Plans, Time Series Data, IoT Sensor Data, etc.)

• Similarity-Based Retrieval Methods for Complex Cases

• Learning of Similarity Measures

• Adaptation Methods for Complex Case Representation, Transfer Learning

• Hybrid AI for Complex Case Representation (e.g., Machine Learning, Deep Learning, and Large Language Models)

• Explainability, Cognition Approaches for Complex Case Representations

• Evaluation of CBR Systems using Complex Case Representations

• Methods for Supporting Users in the Revise-Phase, User Experience

• Tools and Techniques for the Retainment of Complex Cases, Maintenance

• Application Scenarios and Use Cases for Complex Cases (such as Medical Applications, Software Engineering, Administration, Gaming, etc.)

• CBR Frameworks, or Systems supporting Complex Case Representations or Using Real-World Data