By Rosina O. Weber, Michael M. Richter
This publication constitutes the refereed court cases of the seventh foreign convention on Case-Based Reasoning, ICCBR 2007, held in Belfast, Northern eire, united kingdom in August 2007.
The 15 revised complete examine papers and 18 revised poster papers awarded including three invited talks have been conscientiously reviewed and chosen from sixty four submissions. The papers deal with all present elements of case-based reasoning and have unique theoretical study, utilized study, and deployed purposes with sensible, social, environmental or fiscal significance.
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Extra resources for Case-Based Reasoning Research and Development: 7th International Conference on Case-Based Reasoning, ICCBR 2007 Belfast Northern Ireland, UK, August
Example text
Then, KS retrieves results from the data sources in the user-confirmed refined query by dynamically translating it to one or more queries according to each data source’s schema/ontology. The active user can provide feedback on some results and can request another recommendation of the specification. At this time, collaborative filtering can be used because the artifacts of active refined query will have been stored in the case base as a new case, it can then be selected as a similar case because the case’s user query would be identical to the current specification of the active user query.
Also, query translation is needed to retrieve data from the intended heterogeneous sources. Web Services Agent. The main role of the Web Services Agent is to accept a user query that has been refined by consulting the Ontology Agent, and decomposed by the Query Formulation Agent. The Web Services Agent is responsible for the choreography and dispatch of subqueries to appropriate data sources, taking into consideration such facets as: user preference of sites; site authoritativeness and reputation; service-level agreements; size estimates of subquery responses; and quality-of-service measures of network traffic and dynamic site workload [6].
The explanation of a decision tree outcome could be formed by showing the conditions satisfied in the path from the root to a leaf used to classify a new problem. Explanation-based learning (EBL) [28] is a family of methods that build explanations by generalizing examples. In short, EBL solves a problem and then analyzes the problem solving trace in order to generalize it. The generalized trace is an explanation that, in fact, is used as a new domain rule for solving new problems. This explanation is represented using the same formalism as the problems, therefore it is perfectly understandable and usable by the system.