Case-based reasoning
Searching the Web: the public and their queries
Journal of the American Society for Information Science and Technology
Interactive Case-Based Reasoning in Sequential Diagnosis
Applied Intelligence
A Generalised Approach to Similarity-Based Retrieval in Recommender Systems
Artificial Intelligence Review
Integrating Induction and Case-Based Reasoning: Methodological Approach and First Evaluations
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Automatically Selecting Strategies for Multi-Case-Base Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Precision and Recall in Interactive Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
The Case for Graph-Structured Representations
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
The Case for Graph-Structured Representations
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
When Experience Is Wrong: Examining CBR for Changing Tasks and Environments
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Generalized Cases: Representation and Steps Towards Efficient Similarity Assessment
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Knowledge management in case-based reasoning
The Knowledge Engineering Review
Enhancing Case-Based, Collaborative Web Search
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Representing and Retrieving Knowledge Artifacts
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
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We present a new case representation that seeks to make case-based reasoning (CBR) more suited to real world applications. We propose a horizontal representation that is composed of two features, one to represent the problem and one to represent the solution. We also present a similarity metric tailored to our representation. Rather than parametrizing the distance function with weights, it requires one parameter that recommends the cardinality of values for new problems to be solved by the system. Our representation is less restrictive during case acquisition as it does not constrain how non-experts can populate cases and it requires less knowledge engineering effort than the traditional method. We compare our representation to the traditional case representation and show that it is superior when cases are incomplete. Finally, we illustrate the effectiveness of our representation in a real world application, where the demarcation between problem and solution is blurred.