Information Processing and Management: an International Journal - Special issue: history of information science
A context model for knowledge-intensive case-based reasoning
International Journal of Human-Computer Studies - Special issue: using context in applications
Methods and Tactics in Cognitive Science
Methods and Tactics in Cognitive Science
The Role of Information Extraction for Textual CBR
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
CBROnto: A Task/Method Ontology for CBR
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Case Retrieval Nets: Basic Ideas and Extensions
KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Case Representation, Acquisition, and Retrieval in SIROCCO
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Acquiring Word Similarities with Higher Order Association Mining
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Evaluation Measures for TCBR Systems
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Progress in textual case-based reasoning: predicting the outcome of legal cases from text
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Case Retrieval Reuse Net (CR2N): An Architecture for Reuse of Textual Solutions
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Unsupervised feature selection for text data
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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We envisage retrieval in textual case-based reasoning (TCBR) as an instance of abductive reasoning. The two main subtasks underlying abductive reasoning are ‘hypotheses generation' where plausible case hypotheses are generated, and ‘hypothesis testing' where the best hypothesis is selected among these in sequel. The central idea behind the presented two-stage retrieval model for TCBR is that recall relies on lexical equality of features in the cases while recognition requires mining higher order semantic relations among features. The proposed account of recognition relies on a special representation called random indexing, and applies a method that simultaneously performs an implicit dimension reduction and discovers higher order relations among features based on their meanings that can be learned incrementally. Hence, similarity assessment in recall is computationally less expensive and is applied on the whole case base while in recognition a computationally more expensive method is employed but only on the case hypotheses pool generated by recall. It is shown that the two-stage model gives promising results.