Explanation-awareness in Case-Based Reasoning system development aims at making such systems smarter in interactions with their users. When using Linked Data for case acquisition from text one aspect of being smarter is the provision of evidence for the trustworthiness of the acquired cases. The Trustworthiness of such cases relies not only on the provenance of the text but also on the provenance of the used ontological knowledge. Users can only assess the quality of the case-based reasoner’s results, i.e., the cases, if the system provides provenance information and if such a system can justify its results. In fact, explanation capabilities very much rely on provenance information.
myCBR is a freely available tool for rapid prototyping of similarity-based retrieval applications such as case-based product recommender systems. It provides easy-to-use model generation, data import, similarity modelling, explanation, and testing functionality together with comfortable graphical user interfaces. SCOOBIE is an ontology-based information extraction system, which uses symbolic background knowledge for extracting information from text. Extraction results depend on existing knowledge fragments. In this paper we show how to use SCOOBIE for generating cases from texts. More concrete we use ontologies of the Web of Data, published as so called Linked Data interlinked with myCBR’s case model. We present a way of formalising a case model as Linked Data ready ontology and connect it with other ontologies of the Web of Data in order to get richer cases.
T. Roth-Berghofer, and T. Reinartz. Case-Based Reasoning Research and Development: Proceedings of the Fourth International Conference on Case-Based Reasoning, ICCBR 2001, Vancouver, Canada, page 452--466. Berlin, Springer-Verlag, (2001)
T. Roth-Berghofer, and I. Iglezakis. Proceedings of the 8th German Workshop on Case-Based Reasoning, GWCBR 2000, Lämmerbuckel, Germany, page 145--155. Ulm, Germany, DaimlerChrysler, Research and Technology, FT3/KL, (2000)
W. Wilke, I. Vollrath, and R. Bergmann. European Conference on Machine Learning. MLNet Workshop Notes - Case-Based Learning: Beyond Classification of Feature Vectors, page 68--75. Naval Research Laboratory, Washington, D. C., USA, Navy Center for Applied Research in Artificial Intelligence, (1997)