Study and formalization of a case-based reasoning system using a description logic

Salotti, Sylvie; Ventos, Véronique

doi:10.1007/bfb0056341

Cited by 24 publications

(7 citation statements)

References 5 publications

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“…In [7], concepts of a DL are used as indexes for retrieving plans of a case-based planner, and adaptation is performed in another formalism. In [11], a non expressive DL is used for retrieval and for case base organisation. This work uses in particular the notion of least common subsumer (LCS) to reify similarity of the concepts representing the source and target cases: the LCS of concepts ❈ and ❉ is the more specific concept that is more general than both ❈ and ❉ and thus points out their common features.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Case-Based Reasoning. Research and Development

Bichindaritz¹,

Montani²

2010

Lecture Notes in Computer Science

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This paper presents an algorithm of adaptation for a case-based reasoning system with cases and domain knowledge represented in the expressive description logic ALC. The principle is to first pretend that the source case to be adapted solves the current target case. This may raise some contradictions with the specification of the target case and with the domain knowledge. The adaptation consists then in repairing these contradictions. This adaptation algorithm is based on an extension of the classical tableau method used for deductive inference in ALC.

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Section: Discussion and Related Workmentioning

confidence: 99%

Case-Based Reasoning. Research and Development

Bichindaritz¹,

Montani²

2010

Lecture Notes in Computer Science

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“…The inference mechanisms available within DL formalisms, namely subsumption and instantiation, can be used for implementing a CBR system (Salotti & Ventos, 1998; Gómez-Albarrán et al ., 1999). The subsumption relation organizes the concepts of an ontology into a hierarchy, that can be used in CBR as an indexing structure for the case base, for facilitating the retrieval of relevant source cases (Lieber & Napoli, 1996; Napoli et al ., 1996).…”

Section: Protocol Adaptation: From Description Logic Reasoning To Casmentioning

confidence: 99%

Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology

d’Aquin

Lieber

Napoli

2013

The Knowledge Engineering Review

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This article presents the KASIMIR system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL (Web ontology language description logic). The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, for example, because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning (CBR) in the framework of DLs have been used. In addition, the domain of oncology is complex and involves several specialties, for example, surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed DL has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized CBR.

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“…In the last few years, there has been a growing interest in bringing the power and character of DLs into case-based reasoning (CBR) [7,9,17,18]. CBR is a type of analogical reasoning in which new problem is solved by reusing past experiences called source cases.…”

Section: Introductionmentioning

confidence: 99%

“…Adaptation aims at generating a solution for the new problem by adapting the solution contained in the source case. At present, most research is concerned with the retrieval task when introducing DLs into CBR [7,17,18].…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Adapting Cases Represented in a Tractable Description Logic

Chang

Sattler

2014

Case-Based Reasoning Research and Development

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Abstract. Case-based reasoning (CBR) based on description logics (DLs) has gained a lot of attention lately. Adaptation is a basic task in CBR that can be modeled as a knowledge base revision problem which has been solved in propositional logic. However, in DLs, adaptation is still a challenge problem since existing revision operators only work well for DLs of the DL-Lite family. It is difficult to design revision algorithms that are syntax-independent and fine-grained. In this paper, we present a new method for adaptation based on the tractable DL E L ⊥ . Following the idea of adaptation as revision, we firstly extend the logical basis for describing cases from propositional logic to the DL E L ⊥ , and then present a formalism for adaptation based on E L ⊥ . With this formalism, we show that existing revision operators and algorithms in DLs do not work for it, and then present our adaptation algorithm. Our algorithm is syntax-independent and fine-grained, and satisfies the requirements on revision operators.

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Study and formalization of a case-based reasoning system using a description logic

Cited by 24 publications

References 5 publications

Case-Based Reasoning. Research and Development

Case-Based Reasoning. Research and Development

Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology

Algorithm for Adapting Cases Represented in a Tractable Description Logic

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