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

We welcome any submission that deals with using complex case representations that go beyond simple attribute-value case representations. The application of such complex representations implies 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 impacts the use of other AI methods integrated with CBR. In this workshop, participants shall present their research with complex case representations focusing on the general challenges and the impact on the CBR phases. 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