Paper Info
Title
Legal knowledge acquisition using case-based reasoning and model inference
Abstract
Although Case-Based Reasoning comes out in order to solve knowledge acquisition bottleneck, a case structure acquisition bottleneck has emerged, superseding it. Because we cannot decide an appropriate case structure in advance, a framework for CBR should be able to improve a case structure dynamically, collecting and analyzing cases. Here is discussed a new framework for knowledge acquisition using CBR and model inference. Model Inference tries to obtain new descriptors(predicates) with interaction of a domain expert, regarding the predicate as the slots that compose a case structure, with an eye to the function of theoretical term generation. The framework has two features: (1) CBR obtains a more suitable group of slots (a case structure) incrementally through cooperation with model inference, and (2) model inference with theoretical term capability discovers the rules which deal with a given task better. Furthermore, we evaluate the feasibility of the framework by implementing it to deal with law interpretation and certify two features with the framework.
Year
DOI
Venue
1993
10.1145/158976.159003
ICAIL
Keywords
Field
DocType
legal knowledge acquisition,new descriptors,model inference,knowledge acquisition bottleneck,appropriate case structure,case-based reasoning,theoretical term capability,new framework,case structure,case structure dynamically,knowledge acquisition,case structure acquisition bottleneck,knowledge representation,structural dynamics,legal system,nonmonotonic reasoning,case base reasoning,arguments,use case
Data mining,Bottleneck,Knowledge representation and reasoning,Computer science,Subject-matter expert,Model-based reasoning,Artificial intelligence,Non-monotonic logic,Case-based reasoning,Knowledge acquisition,Legal expert system
Conference
ISBN
Citations 
PageRank 
0-89791-606-9
0
0.34
References 
Authors
3
2
Name
Order
Citations
PageRank
Takahira Yamaguti100.34
Masaki Kurematsu2213.17