Tuuurbine: A Generic CBR Engine over RDFS

International audienceThis paper presents Tuuurbine, a case-based reasoning (CBR) system for the Semantic Web. Tuuurbine is built as a generic CBR system able to reason on knowledge stored in RDF format; it uses Semantic Web technologies like RDF/RDFS, RDF stores, SPARQL, and optionally Semantic Wikis. Tuuurbine implements a generic case-based inference mechanism in which adaptation consists in retrieving similar cases and in replacing some features of these cases in order to obtain one or more solutions for a given query. The search for similar cases is based on a generalization/specialization method performed by means of generalization costs and adaptation rules. The whole knowledge (cases, domain knowledge, costs, adaptation rules) is stored in an RDF store

Improving case retrieval by enrichment of the domain ontology

International audienceOne 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 explicitly 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 (http://taaable.fr), 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

Segmentation of Kidneys Deformed by Nephroblastoma using Case-Based Reasoning

International audienceImage segmentation is a hot topic in image processing re- search. Most of the time, segmentation is not fully automated, and a user is required to guide the process in order to obtain correct results. Yet, even with programs, it is a time-consuming process. In a medical context, segmentation can provide a lot of information to surgeons, but since this task is manual, it is rarely executed because of time. Artificial Intelligence (AI) is a powerful approach to create viable solutions for fully automated treatments. In this paper, we define a case-based rea- soning (CBR) that can enhance region-growing segmentation of kidneys deformed by nephroblastoma. The main problem with region-growing methods is that a user needs to place the seeds in the image manually. Automated methods exist but they are not ecient every time and they often give an over-segmentation. That is why we have designed an adap- tation phase which can modify the coordinates of seeds recovered during the retrieval phase. We compared our CBR approach with manual re- gion growing and Convolutional Neural Networking (CNN) to segment kidneys and tumours of CT-scans. Our CBR system succeeded in per- forming the best segmentation for the kidney