Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Agents that reduce work and information overload
Communications of the ACM
Case-based reasoning
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Using Case-Based Retrieval for Customer Technical Support
IEEE Expert: Intelligent Systems and Their Applications
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Detecting discontinuities in case-bases
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Taxonomic Conversational Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Editorial: Detection of semantic conflicts in ontology and rule-based information systems
Data & Knowledge Engineering
Information Sciences: an International Journal
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With the dramatic proliferation of case-based reasoning systems in commercial applications, many case bases are now becoming legacy systems. They represent a significant portion of an organization's assets, but they are large and difficult to maintain. One of the contributing factors is that these case bases are often large and yet unstructured or semistructured; they are represented in natural language text. Adding to the complexity is the fact that the case bases are often authored and updated by different people from a variety of knowledge sources, making it highly likely for a case base to contain redundant and inconsistent knowledge. In this paper, we present methods and a system for maintaining large and semistructured case bases. We focus on a difficult problem in case base maintenance: redundancy detection. This problem is particularly pervasive when one deals with a semistructured case base. We will discuss an information-retrieval-based algorithm and an implemented system for solving this problem. As the ability to contain the knowledge acquisition problem is of paramount importance, our method allows one to express relevant domain expertise for detecting redundancy naturally and effortlessly. Empirical evaluations of the system demonstrate the effectiveness of the methods in several large domains.