Case Based Reasoning: Case Representation Methodologies
TLDR
It is shown that ontology usage in case representation needs improvements to achieve semantic representation and semantic retrieval in CBR system.Abstract:
Case Based Reasoning (CBR) is an important technique in artificial intelligence, which has been applied to various kinds of problems in a wide range of domains. Selecting case representation formalism is critical for the proper operation of the overall CBR system. In this paper, we survey and evaluate all of the existing case representation methodologies. Moreover, the case retrieval and future challenges for effective CBR are explained. Case representation methods are grouped in to knowledge-intensive approaches and traditional approaches. The first group overweight the second one. The first methods depend on ontology and enhance all CBR processes including case representation, retrieval, storage, and adaptation. By using a proposed set of qualitative metrics, the existing methods based on ontology for case representation are studied and evaluated in details. All these systems have limitations. No approach exceeds 53% of the specified metrics. The results of the survey explain the current limitations of CBR systems. It shows that ontology usage in case representation needs improvements to achieve semantic representation and semantic retrieval in CBR system.read more
Citations
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Foundations Of Soft Case Based Reasoning
TL;DR: As one of the part of book categories, foundations of soft case based reasoning always becomes the most wanted book.
Journal ArticleDOI
An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection
TL;DR: In this paper, a decision support system for manufacturing process selection based on ontology-enabled case-based reasoning (CBR) was developed to support process selection in industry 4.0 context.
Journal ArticleDOI
A novel decision-making model for selecting a construction project delivery system
TL;DR: A novel systematic decision-making model to select the appropriate PDS by using the combination of case-based reasoning (CBR) and robust nonparametric production frontier method and can overcome some problems of the traditional methods and improve the accuracy of PDS selection.
A sustainable industrial site redevelopment planning support system
TL;DR: A submitted manuscript is the version of the article upon submission and before peer-review as discussed by the authors, while a published version is the final layout of the paper including the volume, issue and page numbers.
Journal ArticleDOI
Experience capitalization to support decision making in inventive problem solving
Pei Zhang,Amira Essaid,Amira Essaid,Cecilia Zanni-Merk,Denis Cavallucci,Denis Cavallucci,Sarra Ghabri +6 more
TL;DR: This approach is based on the use of the case-based reasoning (CBR) for collecting and rapidly accessing the experiences and demonstrates that it gives better results and improves decision making in the inventive design process.
References
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Journal ArticleDOI
Case-based reasoning: foundational issues, methodological variations, and system approaches
Agnar Aamodt,Enric Plaza +1 more
TL;DR: An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.
BookDOI
The Description Logic Handbook: Theory, Implementation and Applications
TL;DR: The Description Logic Handbook as mentioned in this paper provides a thorough account of the subject, covering all aspects of research in this field, namely: theory, implementation, and applications, and can also be used for self-study or as a reference for knowledge representation and artificial intelligence courses.
Book
Case-based reasoning
TL;DR: Case-based reasoning as discussed by the authors is one of the fastest growing areas in the field of knowledge-based systems and the first comprehensive text on the subject is presented by a leader in this field.
Proceedings ArticleDOI
Verb semantics and lexical selection
Zhibiao Wu,Martha Palmer +1 more
Abstract: This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.
Journal ArticleDOI
Knowledge engineering: principles and methods
TL;DR: The paradigm shift from a transfer view to a modeling view is discussed and two approaches which considerably shaped research in Knowledge Engineering are described: Role-limiting Methods and Generic Tasks.
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