LEADER 00000nam  2200361   4500 
001    AAI3449570 
005    20120516132918.5 
008    120516s2011    ||||||||||||||||| ||eng d 
020    9781124570181 
035    (UMI)AAI3449570 
040    UMI|cUMI 
100 1  Powell, Jay 
245 12 A generic web-based knowledge discovery framework for case
       -based reasoning  
300    179 p 
500    Source: Dissertation Abstracts International, Volume: 72-
       06, Section: B, page: 3537 
500    Adviser: David Leake 
502    Thesis (Ph.D.)--Indiana University, 2011 
520    Case-based reasoning (CBR) systems rely on structured 
       knowledge called cases for reasoning. These cases 
       typically represent examples or prior experiences from a 
       task domain. Acquiring adaptation-relevant knowledge in 
       case-based reasoning systems has proven to be a 
       challenging problem.  Such knowledge is typically elicited
       from domain experts or extracted from the case-base 
       itself.  The first approach is often limited by practical 
       concerns, such as time and cost, while the second is 
       limited by the knowledge present in the case base.  
       Fortunately the Web contains a vast source of knowledge on
       a variety of topics, compiled by experts and volunteers, 
       which can be navigated by artificial agents. This research
       focuses on the problem of acquiring case adaptation 
       knowledge from Web-based resources 
520    The primary hypothesis of this research is that task-
       relevant knowledge for a CBR system can be mined from Web-
       based resources on demand. This dissertation proposes 
       integrating the knowledge discovery process with the 
       traditional case-based reasoning cycle. Knowledge 
       discovery from Web-based resources is then applied to the 
       problem of acquiring case adaptation knowledge for CBR 
       systems. Because the web contains knowledge covering a 
       variety of domains, a generic knowledge discovery 
       framework is required in order to discover and reason 
       about task-relevant knowledge. A generic knowledge 
       discovery framework for case adaptation knowledge is 
       implemented in the system WebAdapt. To increase its 
       generality, WebAdapt relies on minimal pre-coded domain 
       knowledge and acquires all other task-relevant knowledge 
520    This dissertation discusses WebAdapt's basic model and the
       necessary components for a generic knowledge discovery 
       framework for CBR, including (1) automatic detection and 
       recovery from failures in the search process, (2) resource
       selection, and (3) knowledge retention. A set of empirical
       results are then presented to evaluate the efficacy of Web
       mining for case based reasoning. The contributions of each
       component in the framework are also judged based on 
       empirical results 
590    School code: 0093 
650  4 Computer Science 
690    0984 
710 2  Indiana University.|bComputer Sciences 
773 0  |tDissertation Abstracts International|g72-06B 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/