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
CBROnto: A Task/Method Ontology for CBR
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Evaluation Measures for TCBR Systems
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Supervised latent semantic indexing using adaptive sprinkling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Holographic reduced representations
IEEE Transactions on Neural Networks
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Case Based Reasoning(CBR), an artificial intelligence technique, solves new problem by reusing solutions of previously solved similar cases. In conventional CBR, cases are represented in terms of structured attribute-value pairs. Acquisition of cases, either from domain experts or through manually crafting attribute-value pairs from incident reports, constitutes the main reason why CBR systems have not been more common in industries. Manual case generation is a laborious, costlier and time consuming task. Textual CBR (TCBR) is an emerging line that aims to apply CBR techniques on cases represented as textual descriptions. Similarity of cases is based on the similarity between their constituting features. Conventional CBR benefits from employing domain specific knowledge for similarity assessment. Correspondingly, TCBR needs to involve higher-order relationships between features, hence domain specific knowledge. In addition, the term order has also been contended to influence the similarity assessment. This paper presents an account where features and cases are represented using a distributed representation paradigm that captures higher-order relations among features as well as term order information.