Valmi Dufour-Lussier

Text adaptation using formal concept analysis

This paper addresses the issue of adapting cases represented by plain text with the help of formal concept analysis and natural language processing technologies. The actual cases represent recipes in which we classify ingredients according to culinary techniques applied to them. The complex nature of linguistic anaphoras in recipe texts make usual text mining techniques inefficient so a stronger approach, using syntactic and dynamic semantic analysis to build a formal representation of a recipe, had to be used. This representation is useful for various applications but, in this paper, we show how one can extract ingredient–action relations from it in order to use formal concept analysis and select an appropriate replacement sequence of culinary actions to use in adapting the recipe text.

Improving case retrieval by enrichment of the domain ontology

One way of processing case retrieval in a case-based reasoning (CBR) system is using an ontology in order to generalise the target problem in a progressive way, then adapting the source cases corresponding to the generalised target problem. This paper shows how enriching this ontology improves the retrieval and final results of the CBR system. An existing ontology is enriched by automatically adding new classes that will refine the initial organisation of classes. The new classes come from a data mining process using formal concept analysis. Additional data about ontology classes are collected specially for this data mining process. The formal concepts generated by the process are introduced into the ontology as new classes. The new ontology, which is better structured, enables a more fine-grained generalisation of the target problem than the initial ontology. These principles are tested out within Taaable, a CBR system that searches cooking recipes satisfying constraints given by a user, or adapts recipes by substituting certain ingredients for others. The ingredient ontology of Taaable has been enriched thanks to ingredient properties extracted from recipe texts.