Knowledge-Intensive Case-Based Reasoning in CREEK

Abstract

Abstract. Knowledge-intensive CBR assumes that cases are enriched with general domain knowledge. In CREEK, there is a very strong coupling between cases and general domain knowledge, in that cases are embedded within a general domain model. This increases the knowledge-intensiveness of the cases themselves. A knowledge-intensive CBR method calls for powerful knowledge acquisition and modeling techniques, as well as machine learning methods that take advantage of the general knowledge represented in the system. The focusing theme of the paper is on cases as knowledge within a knowledgeintensive CBR method. This is made concrete by relating it to the CREEK architecture and system, both in general terms, and through a set of example projects where various aspects of this theme have been studied.

Applications of CBR in Oil Well Drilling: A General Overview

“…A classical example is the CASEY system, a medical application to diagnose heart failures [4]. Later, other frameworks for building knowledge-based systems that integrate CBR with rule-based reasoning (RBR) and model-based reasoning (MBR) were introduced by other groups such as [5] and [6].…”

Decentralized Case-Based Reasoning for the Semantic Web

“…[3,7,8]), the CBR process relies on a formalized model of domain knowledge. This model may contain, for example, an ontology of the application domain, and can be used to organize the case base for case retrieval.…”

“…CBR is a type of analogical reasoning in which problem-solving is based on the adaptation of the solutions of similar problems, already solved and stored in a case base. In particular, knowledge-intensive CBR (KI-CBR [3]) relies on a knowledge base including domain knowledge and, as well, knowledge units exploited for the retrieval and adaptation operations of CBR.…”

Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems

“…Undoubtedly, conceptual explanations need much more knowledge than provided in most CBR systems, even in knowledge rich systems such as CREEK [33,34], where general (domain-dependent) knowledge is represented as semantic network. Relevance goal fullfilled by a causal why-explanation: The user can further on ask to know why the system asked this specific question.…”