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Author Powell, Jay
Title A generic web-based knowledge discovery framework for case-based reasoning
book jacket
Descript 179 p
Note Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3537
Adviser: David Leake
Thesis (Ph.D.)--Indiana University, 2011
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
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 on-demand
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
School code: 0093
Host Item Dissertation Abstracts International 72-06B
Subject Computer Science
0984
Alt Author Indiana University. Computer Sciences
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