The vocabulary problem in human-system communication
Communications of the ACM
Case-based reasoning
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Peer-to-Peer: Harnessing the Power of Disruptive Technologies
Peer-to-Peer: Harnessing the Power of Disruptive Technologies
OIL: An Ontology Infrastructure for the Semantic Web
IEEE Intelligent Systems
Automatically Selecting Strategies for Multi-Case-Base Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Collaborative Case-Based Reasoning: Applications in Personalised Route Planning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
When Two Case Bases Are Better than One: Exploiting Multiple Case Bases
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Managing Multiple Case Bases: Dimensions and Issues
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Harmonise: A Solution for Data Interoperability
I3E '02 Proceedings of the IFIP Conference on Towards The Knowledge Society: E-Commerce, E-Business, E-Government
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Realizing Modularized Knowledge Models for Heterogeneous Application Domains
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
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The problem of heterogeneous case representation poses a major obstacle to realising real-life multi-case-base reasoning (MCBR) systems. The knowledge overhead in developing and maintaining translation protocols between distributed case bases poses a serious challenge to CBR developers. In this paper, we situate CBR as a flexible problem-solving strategy that relies on several heterogeneous knowledge containers. We introduce a technique called language games to solve the interoperability issue. Our technique has two phases. The first is an eager learning phase where case bases communicate to build a shared indexing lexicon of similar cases in the distributed network. The second is the problem-solving phase where, using the distributed index, a case base can quickly consult external case bases if the local solution is insufficient. We provide a detailed description of our approach and demonstrate its effectiveness using an evaluation on a real data set from the tourism domain.