Analogical Transfer in RDFS, Application to Cocktail Name Adaptation

Nadia Kiani (1) , Jean Lieber (2, 3) , Emmanuel Nauer (3, 2) , Jordan Schneider (1)

1 UL - Université de Lorraine
2 ORPAILLEUR - Knowledge representation, reasonning
3 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications

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?

Mots clés: RDFS; cocktail name adaptation; adaptation; analogical transfer

Domaines: Intelligence artificielle [cs.AI]

Nadia Kiani, Jean Lieber, Emmanuel Nauer, Jordan Schneider. Analogical Transfer in RDFS, Application to Cocktail Name Adaptation. International Conference on Case-Based Reasoning (ICCBR-2016), Oct 2016, Atlanta, United States. pp.218 - 233, ⟨10.1007/978-3-319-47096-2_15⟩. ⟨hal-01410237⟩