Applying Belief Revision to Case-Based Reasoning

Cojan, Julien; Lieber, Jean

doi:10.1007/978-3-642-54516-0_6

Cited by 14 publications

(16 citation statements)

References 22 publications

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“…Here SQTRL rules are used to implement this idea (for both generalizationbased and non generalization-based modifications) when (a part of) the source case can be represented as an RDFS base or a SPARQL query. Another work based on this principle is revision-based adaptation [5], i.e., adaptation based on the use of a belief revision operator [1]. Such an operator associates to two belief bases ψ and µ a belief base ψ µ equivalent to ψ ∧ µ where ψ is the minimal modification of ψ to make it consistent with µ (given some modification metric).…”

Section: Discussion and Related Workmentioning

confidence: 99%

A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

Bruneau

Gaillard²,

Lasolle

et al. 2017

Case-Based Reasoning Research and Development

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Abstract. This paper presents SQTRL, a language for transformation rules for SPARQL queries, a tool associated with it, and how it can be applied to retrieval and adaptation in case-based reasoning (CBR). Three applications of SQTRL are presented in the domains of cooking and digital humanities. For a CBR system using RDFS for representing cases and domain knowledge, and SPARQL for its query language, case retrieval with SQTRL consists in a minimal modification of the query so that it matches at least a source case. Adaptation based on the modification of an RDFS base can also be handled with the help of this tool. SQTRL and its tool can therefore be used for several goals related to CBR systems based on the semantic web standards RDFS and SPARQL.

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Section: Discussion and Related Workmentioning

confidence: 99%

A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

Bruneau

Gaillard²,

Lasolle

et al. 2017

Case-Based Reasoning Research and Development

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“…Modifying a case until it reaches consistency with the target problem wrt the domain knowledge is what revision-based adaptation (RBA) does [3]. Therefore, though RBA has not been a useful guideline for starting this research, it could be used to re-describe this contribution and to go one step further, in order to examine formal properties of the analogical transfer and to propose new strategies.…”

Section: Discussionmentioning

confidence: 99%

“…These three adaptation issues have been addressed thanks to the principle of revisionbased adaptation, i.e., adaptation based on belief revision [3] (the first one has also been addressed using other techniques).…”

Section: Introductionmentioning

confidence: 99%

Analogical Transfer in RDFS, Application to Cocktail Name Adaptation

Kiani¹,

Lieber²,

Nauer³

et al. 2016

Case-Based Reasoning Research and Development

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Abstract. This paper deals with analogical transfer in the framework of the representation language RDFS. The application of analogical transfer to case-based reasoning consists in reusing the problem-solution dependency to the context of the target problem; thus it is a general approach to adaptation. RDFS is a representation language that is a standard of the semantic Web; it is based on RDF, a graphical representation of data, completed by an entailment relation. A dependency is therefore represented as a graph representing complex links between a problem and a solution, and analogical transfer uses, in particular, RDFS entailment. This research work is applied (and inspired from) the issue of cocktail name adaptation: given a cocktail and a way this cocktail is adapted by changing its ingredient list, how can the cocktail name be modified?

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“…This principle is detailed in [5]. Roughly said, a revision-based adaptation consists in modifying the source case minimally so that it is consistent with the query, while taking into account the domain knowledge.…”

Section: Adaptation Of the Ingredient Quantitiesmentioning

confidence: 99%

Taaable: A Case-Based System for Personalized Cooking

Cordier

Dufour-Lussier

Lieber

et al. 2013

Studies in Computational Intelligence

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Taaable is a Case-Based Reasoning (cbr) system that uses a recipe book as a case base to answer cooking queries. Taaable participates in the Computer Cooking Contest since 2008. Its success is due, in particular, to a smart combination of various methods and techniques from knowledge-based systems: cbr, knowledge representation, knowledge acquisition and discovery, knowledge management, and natural language processing. In this chapter, we describe Taaable and its modules. We first present the cbr engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available. Next, we focus on the knowledge containers used by the system. We report on our experiences in building and managing these containers. The Taaable system has been operational for several years and is constantly evolving. To conclude, we discuss the future developments: the lessons that we learned and the possible extensions.

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Applying Belief Revision to Case-Based Reasoning

Cited by 14 publications

References 22 publications

A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

Analogical Transfer in RDFS, Application to Cocktail Name Adaptation

Taaable: A Case-Based System for Personalized Cooking

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